Bibliographic record
Abstract
The ways in which social scientists conceptualize the "reflexive" human subject have important consequences for how we go about our research. Whether and how we understand human subjects to be the authors of our own actions helps to structure what we say about health, health care, and the many other topics addressed in qualitative health research. In this article, I critically discuss assumptions of human reflexivity that are built into qualitative social science of health and medicine. I describe three alternative ways of understanding reflexive thought and human action derived from the theoretical works of Pierre Bourdieu, Bruno Latour, and George Lakoff and Mark Johnson, respectively. I then apply these three different ways of thinking about reflexivity and the acting subject to the analysis of an excerpt of participant observation data from a health services research study of transitions from hospital to home, illuminating the different kinds of analyses that arise from each perspective. I conclude with a call for social scientists to commit to the search for better ways of understanding the human subject, resisting the temptation to "settle" on theoretical statements that close down the path to more sophisticated conceptualizations of human thought and action.
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 | 0.152 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.254 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".