Introduction
A transcript glossary is a working agreement about meaning
An interview transcript contains words used in a particular conversation. A glossary is a controlled explanation of selected terms for a particular audience and purpose. The work between them is not word search. A phrase may recur because it is part of the interview guide, because an interviewer introduced it, or because participants use it to mean different things. A definition assembled from frequency alone can make a local expression sound like an established research construct. It can also erase the difference between a participant’s everyday language and the team’s analytic category. Before asking ChatGPT for a glossary, identify which decisions the research team has already made and which questions remain open. The model can help draft and organize language; it cannot decide that a repeated term has one stable meaning across a study.
Imagine a fictional city tool-lending workshop studying a new reservation process. Some hypothetical visitors say “hold” for a reservation; staff use “hold” for an item temporarily kept at the counter; one person uses it to mean a place on a waitlist. These invented usages are not findings. They show why a glossary entry should state whose usage it describes, in which context, and whether the wording is a direct participant term or a researcher-defined label. If an entry cannot answer those questions, it may belong in an analysis memo rather than in a public-facing glossary. That distinction keeps a useful vocabulary aid from quietly rewriting the study. A useful entry might say that a term occurred in a particular service interaction and was interpreted in two ways during analysis. It need not tell a satisfying story or resolve the disagreement. It needs to help the next reader understand what the team can responsibly carry forward.
References: MacLean et al., “Improving Accuracy of Transcripts in Qualitative Research”; EQUATOR Network, “Standards for Reporting Qualitative Research”
A glossary is not the same thing as a codebook or dictionary
A dictionary gives a word’s general meanings across a language. A codebook defines how a research team applies analytic labels to data. A participant-language glossary may explain a term as people used it in one setting. A public article glossary may translate necessary specialist language for readers. Those artifacts overlap, but they answer different questions. A codebook may need inclusion and exclusion rules precise enough for analysis; a public glossary may need a short explanation and a boundary note. Neither should be silently substituted for the other. If a team asks ChatGPT to “make a glossary,” it should name which artifact it intends to create and whether the output is internal, participant-facing, or public. For internal coding, a reviewer may need to know whether two passages meet the same analytic rule. For a reader glossary, the question may be whether an unfamiliar phrase can be understood without misrepresenting a speaker. The project should not use the same short definition to answer both tasks unless it has been reviewed for both.
This is more than terminology housekeeping. If the project is still coding interviews, an AI-generated entry could influence the analysts before they have settled the meaning of a category. If analysis is complete and the task is plain-language editing, the same model may help make an approved concept easier to explain. Write the purpose at the top of the working file and carry it into the prompt. A “candidate for review” label is different from “approved definition.” A glossary should never gain authority simply because a model formatted it neatly or because a term appears in several transcripts. The owner and status of each definition need to remain visible to the people who use it.
References: CDC, “Plain Language Materials & Resources”; George Washington University, “The Coding Process”
1. Decide What the Glossary Is For
1.1 Separate participant wording from an analytic code
Start with the source of each candidate term. A participant may use an everyday phrase such as “ready at the counter.” The analyst may code that passage as “reservation fulfilment.” A service manager may call the operational stage “pickup eligibility.” Those phrases might refer to related events without carrying the same meaning. Preserve the speaker’s wording in one field, the team’s analytic label in another, and any public explanation in a third. When a transcript phrase becomes a code, note that it is an interpretive step rather than a quotation with a different font. ChatGPT can propose a distinction, but the team must confirm whether the distinction fits the research question and the evidence. A participant term may be valuable precisely because it does not match the analyst’s preferred category. Keep the original form and its locator before adding any explanatory gloss. If a model normalizes spelling or grammar, compare it with the reviewed transcript and retain the difference only when the project has a reason to do so.
In the fictional workshop, imagine a visitor saying, “It was ready when I got there.” That sentence alone does not tell us whether “ready” means the tool had passed inspection, staff had put it on the counter, or the reservation notice had been sent. A researcher should revisit the surrounding exchange, the service context, and other relevant accounts before defining the term. If the analysis has not resolved those possibilities, the glossary can say “used in more than one way; review pending” rather than inventing a crisp definition. This is one place where a polished answer is less useful than an honest unresolved note. The glossary should reflect the state of analysis, not conceal it. A reviewer can ask whether the definition would still fit if the quote were removed. If it would not, the entry may be resting on a single vivid account rather than on the reviewed concept. That does not make the account unimportant; it means the entry should be labeled as an individual usage.
References: Wutich et al., “Rigorous and Systematic Qualitative Data Analysis”; CDC, “Collecting and Analyzing Qualitative Data”
1.2 State whether an entry describes a word, concept, or process
A glossary entry becomes clearer when it names what kind of thing it defines. A word entry records how a term is used in the study. A concept entry explains an idea assembled from multiple observations. A process entry describes a sequence of actions or decisions. The distinction matters because each one needs different evidence. A word can be linked to a passage; a concept should connect to the analysis that supports it; a process description may need an approved service document as well as interview material. Do not let a model blur those evidence types into one sentence that sounds like an official definition. This classification can remain internal. Its purpose is to stop a public copy edit from changing the underlying research label and to make it clear when an explanation is a communication choice rather than a code definition.
Use a short type label in the internal record, even if the public glossary does not display it. For example, “participant term,” “analytic concept,” “service label,” and “editorial explanation” help reviewers ask the right question. “Tool readiness” might be a project concept, while “ready” is a word used by several speakers, and “reservation confirmed” may be the service’s own phrase. The fictional labels here are invented. In a real project, the approved analysis and authoritative service materials would decide what belongs in each field. When two types are being combined, say so and record who approved the combination. That small distinction prevents the glossary from presenting a researcher’s framing as if it were the participant’s vocabulary. If two roles use the same phrase differently, preserve role or setting only when that detail is approved and useful. Do not infer a hierarchy from job title alone. A glossary can say that meanings varied by context and leave the team to decide whether a later analysis should split the entry.
References: EQUATOR Network, “COREQ”; EQUATOR Network, “Standards for Reporting Qualitative Research”
1.3 Match the definition to its reader and release stage
The same term may need different explanations for an analysis team and a public reader. Researchers may need a precise definition, a scope boundary, and examples of passages that do not fit. A public reader may need one sentence, a concrete example, and a link to fuller methods. Participants may need language that reflects how the project explained the topic to them. Do not assume that one entry can serve all audiences without revision. If the glossary is an internal analysis aid, keep it within the approved research environment. If it is for a public article, confirm the study’s communication plan and the conditions under which participant language can be shared. For example, a participant-facing version might explain what the project means by a term without displaying the full analytic code. A public version may need a note that definitions are specific to one study. An internal version can retain source IDs that would distract or expose information in a public post.
Consent to take part in an interview does not automatically answer every question about later quotation, secondary processing, or public explanation. Check the project’s consent materials, protocol, data-owner conditions, and local review process. If an external AI service would receive any source text, confirm that the proposed use fits the authorized data path before sending it. A model cannot grant that permission by rewriting names or producing a privacy disclaimer. If the release status is not clear, the team can work from already approved, minimized definitions or keep the draft internal while the responsible person decides. The glossary’s intended audience should be fixed before ChatGPT sees material, not guessed after it has returned a polished set of entries. A clear audience statement also helps prevent the draft from drifting. If a glossary begins as an internal aid and is later proposed for a website, treat that as a new publication decision. Review its examples, disclosure risk, permissions, and explanatory language again.
References: GOV.UK, “Getting Informed Consent for User Research”; U.S. HHS, “The Belmont Report”
2. Prepare a Traceable Term Set
2.1 Start from reviewed analysis, not a raw transcript search
A transcript is a record, not a ready-made glossary. It contains interviewer prompts, conversational repetitions, abandoned ideas, names, and details that may have no role in the article. Start with terms the research team has already considered during analysis, then point each candidate to the approved analytic memo or authorized passage. This gives ChatGPT a narrower task: draft language from reviewed concepts rather than decide what the study is about. If analysis is incomplete, the output should stay provisional and internal. Do not present model-selected frequent words as findings or assume that a list of recurring phrases represents the most important concepts. The term list can be produced by analysts after they have read the material and compared relevant passages. It should not be a model’s unattended list of the most frequent nouns. A frequent word may be a prompt artifact, and a rare word may carry an important distinction that only the analysis can explain.
The source chain still matters. When audio or video has been transcribed, the team should retain the transcript version and its review status in its approved project records. WhisperAITranscribe is an audio and video transcription tool; in this workflow, its transcript would remain source material to check, not a glossary, analytic codebook, or research conclusion. Record any corrections and the relation between the transcript and the reviewed term list. A model should receive only the fields required for the drafting task. If the approved concept ledger is sufficient, there is no reason to send an entire recording, transcript, identity key, or unrelated interview material simply because the file is available. A narrow packet might include only an approved definition candidate, its context note, an anonymized source locator, and one cleared example. If a reviewer needs more context, the team can return to its authorized archive rather than placing the full corpus into the prompt.
References: UK Data Service, “Prepare Your Data Collection for Deposit”; CDC, “Collecting and Analyzing Qualitative Data”
2.2 Keep examples connected to their setting and source
Each candidate entry needs a route back to where it came from. Keep a stable term ID, transcript or analytic-source version, authorized location marker, speaker or group code where appropriate, and a note about the surrounding question. The public glossary usually should not expose internal IDs or detailed locators, but the working file should retain enough information for a reviewer to check the wording. A sentence extracted without its question can be misleading: an interviewer may have introduced the term, the participant may have been agreeing tentatively, or the speaker may have corrected their own description later in the exchange. Stable locators matter when a term appears in several versions of a transcript. A page number can change after edits, so a project may use a participant code, interview ID, transcript version, and turn or line marker. Use whichever method the approved repository supports, and avoid inventing a universal locator scheme.
The example should show use without pretending to represent everyone. A team might keep one typical passage, one boundary case, and a note about variation in its internal record. It should not publish all three automatically. In the fictional workshop, “pickup window” could refer to the hours listed in a notice for one visitor and to a much narrower staffed period for another. The phrase is hypothetical; it demonstrates why context belongs near the definition. ChatGPT can organize permitted examples into a draft field, but it should leave the source ID in place and flag missing context rather than creating an illustrative quotation. Reviewers should be able to move from every example back to its approved source before they sign off on the entry. A useful internal note can separate an example that illustrates the definition from an example that challenges it. If there is no approved quotation, the draft should say so. The model should not fill the field with a plausible sentence merely to make every entry look complete.
References: George Washington University, “The Coding Process”; Wutich et al., “Rigorous and Systematic Qualitative Data Analysis”
2.3 Remove identifiers and reduce unnecessary detail
Replacing names with participant numbers is not the same as removing identification risk. A rare occupation, precise location, unusual event, service role, or sequence of dates may identify a person when combined with outside knowledge. Review candidate passages for these contextual clues before using them in a model-assisted step. Keep the identity key separate from transcripts and working glossary files, and do not include it in the prompt. If a term’s meaning can be explained from an approved analytic summary, use that instead of a distinctive quotation. If context is essential but sharing is not authorized, keep the review inside the project’s approved environment. A risk review should consider the combination of details, not just a checklist of names and emails. A distinctive phrase can also be searchable, and a service event may be known to a small community even if the transcript contains no direct identifier.
Publication is a separate decision from internal analysis. A passage that is manageable in a controlled research record may be too revealing in a public glossary that can be copied, searched, and combined with other information. The team should reassess the final combination of details after editing, because a shortened quote can become more recognizable rather than less. Official text-anonymization guidance can help reviewers think about direct and indirect identifiers, but it cannot certify a specific term or excerpt as anonymous. If the team cannot preserve meaning without exposing someone, use a more general description or omit the example. Record the decision in the appropriate project file rather than asking the model to label the passage “safe.” If an example is removed, retain the reason in the controlled review record. This prevents a later writer from reintroducing it because the definition seems abstract. The public glossary can remain concise while the project retains the rationale for its editorial boundary.
References: UK Data Service, “Anonymisation for Text Data”; ICO, “How Do We Ensure Anonymisation Is Effective?”
3. Ask ChatGPT for a Draft, Not an Authority
3.1 Give the model a defined entry shape
A useful prompt makes the expected fields explicit. Ask ChatGPT to return a candidate term, its type, a draft definition, a scope note, the source IDs it used, a representative example if one is approved, a counterexample or exclusion, and questions that need a researcher’s decision. The working format can be plain prose, a spreadsheet, or structured text; the format matters less than keeping each field separate. In particular, do not combine the definition, evidence, and approval status into one fluent paragraph that makes a suggestion look final. The prompt should also say what counts as a refusal or uncertainty. For instance, if the source packet does not distinguish a reservation from a waitlist, the model should return that limitation rather than use outside knowledge to select one meaning.
Tell the model to use only the supplied approved term set, retain the study’s own wording where requested, and mark absent evidence as “not supplied.” It should not invent a quotation, infer a frequency, broaden a local term into a universal one, or add a definition from general knowledge without labeling it as external context. A prompt can ask for two alternatives when the wording is ambiguous, but it should not ask the model to choose which meaning is correct. OpenAI’s prompt guidance supports clear instructions and iteration; NIST describes risks that still make output review necessary. These sources do not guarantee a correct glossary. A good draft is one a reviewer can challenge line by line, not one that sounds unusually certain. The generated output should keep the source IDs beside each proposed field, not only in a final note. A reviewer can then test each definition without reconstructing how the answer was produced. If the model cannot match a term to an approved ID, it should label the entry unsupported.
References: OpenAI, “Prompt Engineering Best Practices for ChatGPT”; NIST, “Generative AI Profile”
3.2 Ask for plain language without changing the concept
A definition can be readable and still be wrong. Ask ChatGPT to shorten specialist wording only after the team has settled the underlying concept, and compare the revision with the approved version. A plain-language alternative should keep the same subject, conditions, and exceptions. If “available” means that the item passed inspection and is actually ready for collection, replacing it with “there” may remove a crucial condition. If a participant uses a metaphor, the public definition should not convert it into a technical claim without evidence. The model can propose a simpler sentence and explain what it changed; a researcher decides whether the simplification keeps the intended meaning. A readability edit should not expand a term’s scope. Changing “some visitors in this project” to “visitors” removes a boundary. Changing a local service label into a common industry term may also imply a standard the project never studied.
The audience also matters. A public reader may need a short definition, while an analyst needs the distinctions that keep a code consistent. CDC plain-language guidance recommends considering reader and purpose when selecting words and organization. That is a useful communication principle, not a glossary standard for every discipline. Preserve technical terms that carry a specific meaning, explain them when needed, and avoid substituting a familiar word that changes the concept. In the fictional tool-lending project, “reservation release” might sound clearer as “the time an uncollected reservation becomes available again,” but that wording should be used only if it matches the actual service rule. In the invented example there is no real rule to verify, so the sentence remains a demonstration, not an operational fact. A plain-language check can ask a representative reader what they think the definition means, but that is a communication test, not a validation of the research finding. If such a test is conducted, record who took part, what version they saw, and what the test can and cannot establish.
References: CDC, “Plain Language Materials & Resources”; GOV.UK, “Sharing User Research Findings”
3.3 Keep uncertainty and competing meanings visible
If the supplied evidence supports two uses, the draft should preserve both. Ask ChatGPT to place alternatives side by side and name the source IDs or contexts associated with each. It can suggest that a term be split, qualified, or left unresolved, but it should state what evidence would be needed to decide. A definition such as “participants use ‘ready’ to describe a completed service stage” may be safer than a sentence that assigns one precise meaning to every speaker. If some speakers mean “staff have prepared the item” and others mean “I can collect it now,” keep those distinctions visible until the research team decides whether they matter to the study. A useful “needs review” note names the missing decision: whether two terms are synonyms, whether the example is releasable, or whether the analysis supports a shared meaning. That gives the researcher a next action without making ChatGPT the arbiter.
Do not turn a counterexample into a footnote merely to make the glossary look consistent. A term’s variation may be analytically important or may simply reflect different settings; either way, the decision belongs to the team. NIST’s risk profile is a reason to inspect generated text for invented details, not proof that every output is unreliable. Research guidance on code definitions can help reviewers examine boundaries and examples, but a glossary is not automatically an analytic codebook. If ChatGPT merges entries, ask it to show the original term IDs and the reason for the proposed merge. The final record should preserve the reviewer’s decision and the version of the evidence used, including a decision to leave two meanings separate. If the model has merged two items, ask it to show which source IDs and original phrases were combined. Then a researcher can reject the merge, split it by context, or approve it with a written reason. The original candidate records should remain available through the revision. A transcript created with WhisperAITranscribe supplies source provenance only; the glossary meaning remains a research-team decision.
References: Wutich et al., “Rigorous and Systematic Qualitative Data Analysis”; NIST, “Generative AI Profile”
4. Review the Entries Against the Analysis
4.1 Verify every definition and example against its source
Read each draft entry beside its authorized source and the approved analysis. Check whether the speaker actually used the term, whether the surrounding exchange changes its meaning, whether the passage is a direct quotation or a researcher paraphrase, and whether a condition has disappeared. If a definition contains a claim about how often a term was used, verify the count and its denominator in the project’s own analytic record. Do not infer frequency from a model’s summary. If a source ID is missing, mark the field unsupported rather than searching for a similar phrase and treating it as confirmation. The scope check should include grammar as well as facts. A definition may quietly change “some participants described” into “the service is,” or change a reported perception into an objective property. Restore the subject and attribution the evidence supports.
A practical review can classify each field as supported, fair editorial wording, unresolved, or unsupported. Supported wording can proceed subject to release checks. Fair editorial wording needs a note showing how it preserves the approved concept. Unresolved wording needs a decision owner. Unsupported text should be removed or returned to analysis. CDC’s qualitative-analysis guidance describes repeated reading, coding, memoing, and checking alternative conclusions as parts of analysis; a glossary draft cannot replace those steps. The team should also compare important entries against the original audio or its verified transcript where the project process requires it. A generated definition that sounds more polished than the source deserves the same scrutiny as any other interpretation. When audio is available and the review protocol requires it, compare the transcript passage with the recording or a verified correction record. A disagreement in transcription may alter a term. Do not let a language model resolve what a person said without the project’s chosen verification method.
References: CDC, “Collecting and Analyzing Qualitative Data”; MacLean et al., “Improving Accuracy of Transcripts in Qualitative Research”
4.2 Keep a glossary from becoming a second codebook by accident
A codebook directs analysis. It explains what analysts should code, when a category applies, and what does not belong. A glossary explains terminology to a named audience. A team may maintain both, but it should not let a public definition become the rule analysts use without review. If a glossary entry starts to specify inclusion and exclusion criteria for interpreting data, mark it as a possible codebook change and route it through the project’s analysis process. Conversely, if a code label is too technical for a public reader, explain it outside the codebook rather than silently renaming the analytic category. For this source chain, a transcript from WhisperAITranscribe is provenance, not evidence that a term has one population-wide meaning. If a glossary term is later used as a code, check whether its definition includes the rule analysts need to apply it consistently. If not, keep the glossary separate and route the proposed codebook change through the analysis team.
The boundary can be subtle. “A missed pickup” may be an everyday expression, a service event, or a code for a defined class of passages. Those are not interchangeable. Keep separate fields for the term people use, the code that the team applies, the concept the study reports, and a plain-language explanation. If one entry relates to another, show that relationship as a proposed link and ask an analyst to approve it. COREQ and SRQR encourage transparent reporting of qualitative work, but neither prescribes a glossary schema. Their role here is to remind the team that terminology should not obscure how the study was conducted or how it reached its claims. The relationship between entries can also be a source of overstatement. A model may make one term a parent category and another a subtype because the hierarchy looks tidy. Ask whether that relation comes from the approved framework or is only a drafting convenience.
4.3 Check omissions, disagreement, and terms that did not survive
A glossary review should inspect what the model left out as well as what it included. Did it omit a less common term that changes the interpretation? Did it merge words that different groups used differently? Did it include an interviewer’s phrase as if a participant had introduced it? Did a concept disappear because it was difficult to define in one sentence? Compare the candidate list with the reviewed analysis, not only with the model’s draft. A short note explaining why a term was excluded can prevent the same debate from returning in a later article or translation. A transcript prepared with WhisperAITranscribe remains a source record to review, not a decision about what a research concept means. An omitted word may also be deliberate. The team may have excluded a label because it was interviewer language, too sensitive to publish, or not analytically relevant. Record that decision so a future model-assisted pass does not add it back as a seemingly obvious glossary term.
Avoid using a glossary to imply that all participants share one vocabulary. If the study includes different roles, locations, languages, or service stages, an entry may need to identify the context in which a term was used. A difference is not automatically an error to reconcile. It could reflect a real variation, an ambiguous question, or an analytic issue that needs further review. COREQ and SRQR are reporting resources rather than instructions to publish every variation. UK qualitative-research ethics guidance also sits in a particular professional and jurisdictional context. The team should decide which differences matter to the reader and retain enough detail to explain its choice. If it cannot do that safely, it can omit the example while keeping the internal source map. Where language or role differences matter, the published note can state that the glossary is specific to the context studied. That is more accurate than forcing a false consensus. The internal record should retain enough detail for the research team to explain that choice.
References: EQUATOR Network, “COREQ”; UK Statistics Authority, “Ethical Considerations Associated with Qualitative Research Methods”
5. Protect Participants and Keep the Glossary Current
5.1 Review participant examples and permissions separately
A glossary can define an approved concept without quoting a participant. If a direct quotation is useful, check the exact wording against the reviewed transcript version, retain its source locator, and confirm that its use fits the consent and publication conditions. A quote discussed during internal analysis may not be appropriate for an external website. A paraphrase can also expose someone if it preserves a rare role, location, event, or combination of details. The team should ask whether the example is necessary, whether it changes the reader’s understanding, and whether a less distinctive account would serve the same purpose. Quotation permission should be checked for the exact proposed use, not inferred from a general wish to share study results. A participant may have agreed to anonymous reporting but not to a searchable direct quote. The project’s actual consent and review documents control.
If a public entry uses a composite or constructed example, say so plainly. Do not invent a sentence and put it in quotation marks, and do not imply a composite is one person’s exact wording. Research ethics guidance supports careful attention to participant interests, but a general principle cannot authorize a particular quote. The project’s approved consent materials, review process, and data-owner requirements decide what may be shared. When a term can be explained without an individual account, use the lower-detail version. A glossary should make terminology easier to understand, not create a new route for publishing sensitive material. The decision and its owner belong in the controlled project record, not only in a chat transcript. A paraphrase can still carry someone’s distinctive story. Review the content after shortening, translating, or combining it with another detail. If a public reader could plausibly recognize the person, reconsider whether the example adds enough value to justify the exposure.
References: U.S. HHS OHRP, “Informed Consent FAQs”; UK Data Service, “Anonymisation for Text Data”
5.2 Record versions, ownership, and changes to meaning
A glossary needs an owner and a change history. Record who proposed an entry, which analysis version supports it, who reviewed the definition, whether the entry is internal or public, and when it was last checked. Keep the model prompt and generated draft if the project’s retention rules allow, but distinguish them from the approved source and final human decision. If an analyst narrows a term, splits it into two uses, or removes a quotation, note the reason and the evidence that prompted the change. A later writer should not have to infer whether a clean-looking definition was proposed by ChatGPT or approved by the research team. Versioning is especially useful when analysts disagree. Instead of overwriting one definition, retain the proposed wording, the decision, the reviewer, and the source version. A later team can see whether a change reflected new evidence or only a copy edit.
Access and retention conditions vary by service and account, and they can change. Confirm the actual workspace and data-use settings before sending approved material to an external model. This check does not itself establish institutional permission. When audio or video has been transcribed, WhisperAITranscribe is the upstream transcription stage; its transcript, ChatGPT’s candidate glossary, and the team’s approved definitions are separate artifacts. Keep version links between them without merging their authority. If a model invents an entry or removes a scope qualifier, preserve the correction in the review record and update the glossary version that readers will actually use. A data-management template can help teams document the process, but it cannot decide the project’s rules. Do not include secret keys, raw identifiable text, or a complete transcript merely to make a prompt reproducible. Reproducibility should operate within the project’s approved record system. The prompt can point to a controlled source ID without duplicating the protected source.
References: AAPOR, “2026 Code of Professional Ethics and Practices”; UK Data Service, “Managing Data: Tools and Templates”
5.3 Revisit language when audience or context changes
A definition that works for researchers may not work for participants, service staff, or readers outside the study’s field. Before reusing an entry in a new report or language, check that its purpose and scope still fit. Do not assume a direct translation preserves a concept. A term may have no exact counterpart, may carry a different association in another language, or may be used differently by a group that was not part of the study. When that happens, preserve the original term where appropriate and explain the local meaning rather than forcing a convenient substitute. If a term first appeared in a transcript from WhisperAITranscribe, record that source version separately. A WhisperAITranscribe transcript remains distinct from a human-approved definition. A translated glossary should retain a note about terms that do not map cleanly. Translators can propose alternatives, but the researchers should review whether the concept’s boundary survives. A reader may need both the original expression and a short explanation of why a direct equivalent was not used.
If the glossary is published, create a review date and a responsible owner. Service language can change; new research may challenge an earlier definition; a reader may identify an ambiguity. A correction should update the visible version and the underlying source map together. AAPOR’s professional standards and data-preparation guidance support transparent, accountable research practice, but they do not set a universal change-control process. For the project, record what changed, why it changed, which audience is affected, and whether the correction alters a study claim. If a source transcript came from WhisperAITranscribe, preserve that version in the provenance note without presenting it as a definition source. An update process can also specify what triggers review: new data, a changed service, a correction to transcription, a participant concern, or a new audience. The trigger should be proportionate; a wording clarification does not always require reanalysis, while a changed concept might.
6. Conclusion
6.1 Let the reviewed evidence set the definition’s limits
When audio or video is part of the source chain, WhisperAITranscribe is an audio and video transcription tool upstream of analysis. ChatGPT can help draft glossary entries from an approved term set, suggest shorter wording, and flag places where definitions overlap. It cannot determine what a participant meant, decide that a phrase is a finding, or authorize the use of a quotation. Start with the audience and artifact type. Keep participant wording separate from analytic codes and public explanations. Give the model only the reviewed material it needs, keep source IDs attached, and ask it to mark ambiguity rather than resolve it by guesswork. Then check every definition, example, scope note, and relationship against the analysis and release conditions. A concise record of unresolved terms can be more useful than a glossary that claims completeness. It tells future writers where further evidence is needed and stops a reader from treating a missing definition as proof that the term was irrelevant.
A glossary is useful when it makes a team’s decisions easier to understand and revisit. It becomes risky when its concise entries look more universal than the study supports. Keep variations visible where they change meaning; state when a term is project-specific; and leave uncertain entries pending until the right analyst reviews them. If a reader cannot tell whether an explanation is participant language, a code, or an editorial translation, the entry needs another pass. The model’s formatting is not approval. The research team remains responsible for what the term means in the project and how that meaning is presented to an audience. If the team cannot trace an entry to reviewed material, leave it out of the approved glossary. It can remain a question for analysis, but it should not travel as established terminology simply because it fits the page.
6.2 Follow four steps that a reviewer can retrace
Step 1: Name the glossary’s audience and purpose, then label each candidate as participant wording, analytic code, study concept, service term, or public explanation. Step 2: Prepare a minimal, reviewed term set with source IDs, transcript version, context, variation, privacy status, and the release boundary; if the source came from WhisperAITranscribe, record that provenance separately. Step 3: Ask ChatGPT for draft definitions, scope notes, approved examples, exclusions, and open questions using only the supplied material; require it to flag missing evidence instead of filling gaps. Step 4: Have the research team compare every field with the analysis, verify quotes and permissions, approve the intended audience, and record changes before publication. These steps make the draft reviewable; they do not guarantee correctness or permission. The first two steps set the authority boundary before any generation. The third makes the model’s task constrained and inspectable. The last keeps the human decision with the people who understand the study, its participants, and its publication conditions.
The order matters. Removing a name after an unauthorized transfer does not repair the earlier decision. A definition written for a public audience may not be suitable for an internal coding rule. A candidate term can become an analytic category only through the team’s process, not because a model placed it under a bold heading. Keep draft, approved, and published versions distinguishable. If the study is still developing its analysis, label the glossary provisional and keep it in its permitted setting. If an entry depends on a quote that has not been cleared, remove that example or leave the entry pending. A concise status note can prevent an unfinished suggestion from travelling as established terminology. If an analyst changes a definition after publication, update the article or explain the revision according to the site’s editorial policy. A glossary is not permanently correct because it was once approved; its scope remains tied to the evidence and the date of the study.
6.3 Keep each term connected to its speaker, evidence, and purpose
A transcript does not arrive with a ready-made glossary. Researchers select terms, decide what kind of definition is needed, and explain how the word was used in context. WhisperAITranscribe is an audio and video transcription tool upstream of that work; WhisperAITranscribe does not decide what a participant meant or approve a public definition. ChatGPT can help organize a reviewed set of candidate entries, but each definition should remain connected to the evidence and the people responsible for the analysis. Preserve differences when they matter, identify uncertainty when it remains, and let the final glossary say only what the study can support. That connection is the practical test for every entry: a researcher should be able to explain where the wording came from, what the team decided, and which limits travel with it. If the source trail is missing, the next task is to restore it or remove the claim.
References: CDC, “Plain Language Materials & Resources”; EQUATOR Network, “Standards for Reporting Qualitative Research”