The researcher as instrument - how our capacity for empathy supports qualitative analysis of transcripts
Bibliographic record
Abstract
In this article we draw on literature from philosophy, history, and psychology to argue that empathy supports qualitative analysis of transcripts in several ways. We discuss examples of these processes both with data that we have co-created with our participants and data where we have not interacted with participants during data collection. What we suggest is not a new approach to analysis. Rather, we argue that the deliberate use of empathy bears potential to strengthen analysis across various analytical approaches. We explore five examples of how to access and harness our capacity for empathy as a resource in qualitative analysis: 1) Make time and room for prolonged engagement with data; 2) Use details and context actively when developing your understanding; 3) Practice decentering by actively seeking the perspective of the participants; 4) Attend to your own visceral experiences and body sensations; and 5) Utilize your capacity for imagination and creativity.
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 | 0.299 | 0.382 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".