Exploring Identity: What We Do as Qualitative Researchers
Bibliographic record
Abstract
Although there has been much discussion about distinctions between quantitative and qualitative research, our purpose here is not to revive those conversations, but instead to attempt to explore and articulate our identities as researchers who practice in the qualitative tradition. Using autoethnography as our methodology, we as six researchers from various social science disciplines and at various career stages engaged in focused introspection by responding individually to two questions: who am I as a qualitative researcher; and how did I come to that understanding? This reflection led to discussions of those elements and experiences that have shaped the way we see ourselves in the context of our research. The question of “identity” evolved into a discussion about “what we do.” During our data analysis, six themes emerged, representing our group’s responses: (a) building epistemology, (b) making/doing good research, (c) as an art or craft, (d) why does qualitative research need legitimating? (e) qualitative research as a social bridge, and (f) stewards of people’s lived experience. We conclude by reflecting on the value of building a community of practice among qualitative researchers.
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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 | 0.388 | 0.396 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.023 | 0.059 |
| Scholarly communication | 0.031 | 0.034 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".