A Quick Guide to Speed-Dating Theorists through Thinking with Theory in Qualitative Research: Viewing Data Across Multiple Perspectives
Bibliographic record
Abstract
Searching for a way to read, think, research, and write with complex theory, the authors of this book review came together for a peer-led doctoral reading group. Given our disparate disciplinary commitments, as well as our uncertainty as to how to embark on such a task, our group coalesced around the approach offered by Alecia Y. Jackson and Lisa A. Mazzei in Thinking with Theory in Qualitative Research: Viewing Data Across Multiple Perspectives. Jackson and Mazzei implicitly propose the format of speed-dating theorists within their book, which we found ideal for our theoretically promiscuous reading group. We offer a window into our speed-dating experiences through a creatively flirty medium: dating service profiles. Like the profiles, the productivity of using Thinking with Theory as a guide for promiscuous theoretical thinking, researching, and writing is not in its prescription but rather in the emergence of different productions of knowledge that occur relationally.
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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 | 0.037 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.040 | 0.027 |
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".