Method in limbo? Theoretical and empirical considerations in using thematic analysis by veterinary and One Health researchers
Bibliographic record
Abstract
This article spans a number of theoretical, empirical and practice junctures at the intersection of human and animal medicine and the social sciences. We discuss the way thematic analysis, a qualitative method borrowed from the social sciences, is being increasingly used by veterinary and One Health researchers to investigate a range of complex issues. By considering theoretical aspects of thematic analysis, we expand our discussion to question whether this tool, as well as other social science methods, is currently being used appropriately by veterinary and human health researchers. We suggest that additional engagement with social science theory would enrich research practices and improve findings. We argue that considerations of 'big theory' - ontological and epistemological positionings of the researcher - and 'small(er)' theory, the specific social theory in which research is situated, are both necessary. Our point of departure is that scientific discourse is not merely construction or ideology but a unique and continuing arena of debate, in part at least because of the elevation of self-criticism to a central tenet of its practice. We argue for further engagement with the core ideas and concepts outlined above and discuss them in what follows. In particular, and by way of focusing the point, we suggest that for veterinary, One Health, and human medical researchers to use thematic analysis to its maximum potential they should be encouraged to engage with both broader socio-economic theories and with questions of ontology and epistemology.
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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 | 0.595 | 0.499 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.014 | 0.100 |
| Scholarly communication | 0.033 | 0.042 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".