Advancing the Impact of Critical Qualitative Research on Policy, Practice, and Science
Bibliographic record
Abstract
Discourses of research impact shape the ways in which critical qualitative research and researchers are evaluated in contemporary academic environments. Mainstream conceptualizations of research impact arise from a positivist perspective that challenges the aims and approaches of critical qualitative research. In this paper, we propose a framework for conceptualizing the impact of critical qualitative research on policy, practice, and science. After critiquing literature that presents mainstream views on research impact, we summarize a recent framework for conceptualizing the impact of critical research specifically. We then add to the Machen framework by highlighting the impacts of critical qualitative research on the institutions and practices of science. We provide examples of ways in which researchers at the Centre for Critical Qualitative Health Research at the University of Toronto have made contributions to the impact of critical qualitative research on science, and conclude by addressing implications of this framework for the ways in which critical qualitative researchers can plan and evidence the impact of their work.
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 | 0.700 | 0.670 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.024 | 0.163 |
| Scholarly communication | 0.055 | 0.064 |
| Open science | 0.008 | 0.048 |
| Research integrity | 0.015 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".