Bibliographic record
Abstract
In this case study, I address the procedural ethics of conversation analysis (CA) and the collection of naturally occurring mundane interactions. I draw from the challenges that emerged from the institutional ethics review of the HIV, health and interaction study (the H2I Study), a CA project that sought to identify the practices through which normative assumptions of HIV and other health conditions are produced in conversations. Consistent with CA’s preference for naturally occurring interactions, the H2I Study collected and analysed everyday telephone calls involving people living with HIV. This article offers practical strategies CA researchers might use to navigate two ethical concerns raised about the collection of naturally occurring mundane interactions. The first questions the merits of collecting naturally occurring mundane interactions. For those unfamiliar with CA, the specific advantages of analysing naturally occurring mundane interactions may not be self-evident. This places an evidentiary burden on CA researchers to warrant the collection of this type of data. To address this concern, I suggest demonstrating in ethics applications the analytic value of CA using publicly available interactions. The second concern questions the use of verbal consent necessary for the collection of naturally occurring mundane interactions. Like most CA research, the H2I Study required flexible informed consent protocols appropriate for spontaneous and unpredictable interactions. Drawing from within and outside the CA literature, I offer three rationales for the use of verbal consent. This article is written as a practical resource for conversation analysts seeking approval from their research ethics board (REB) and for REBs who might be unfamiliar with CA research. This article contributes to a small but growing body of literature that documents not only the kinds of challenges CA researchers encounter from institutional ethics review, but the specific procedural ethics they may employ to secure ethics approval.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.272 | 0.382 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".