Doing Science Differently: A Framework for Assessing the Careers of Qualitative Scholars in the Health Sciences
Bibliographic record
Abstract
Hiring and promotion of qualitative researchers in the health sciences, in Canada and internationally, is impacted by the prestige of quantification as the ultimate measure of scientific quality in current academic and health-care settings. This is further exacerbated by neoliberal notions of productivity, which offer very limited forms of assessment for different ways of producing knowledge or doing science differently. While qualitative researchers share the effects of the politics of productivity and corporate university policies with other academics, we argue that they are disadvantaged by the combination of the latent biomedical conservatism that characterizes the health sciences in Canada with the lack of frameworks to acknowledge and properly assess alternative forms of interdisciplinary scholarship. In our experience, it is challenging for qualitative researchers to advance in Canadian health sciences faculties. In light of this, we propose a framework for evaluating their scholarly work. We have structured this article in three sections: (a) to characterize the academic landscape in which qualitative health scholars find themselves when housed in Canadian faculties of medicine and their schools of health sciences, (b) to report on an organizational scan we undertook in order to understand current practices of evaluating scholarly productivity at these institutions, and (c) to propose a set of criteria that could more appropriately evaluate the contributions made by qualitative researchers working in the health sciences.
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 | 0.283 | 0.196 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.028 | 0.017 |
| Science and technology studies | 0.028 | 0.076 |
| Scholarly communication | 0.035 | 0.018 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".