Post-Normal Science, Post-COVID: Multi-Method Approaches to Actionable Research
Bibliographic record
Abstract
Researching complex public health issues, particularly in rural settings, requires pragmatic approaches that emphasize local perspectives, actionable findings, and timely knowledge mobilization. This paper presents a multi-phased, place-based methodology employed by a practitioner-researcher to conduct qualitative research, share findings in real time, and co-create practical recommendations in a large rural geography. This paper describes the study’s use of post-normal science and outlines the methodology from conceptualization to conclusion. Next, it comments on both scientific and political rigour of the research, using contributions from the lead author’s reflective journal. With empirical findings from the study reported elsewhere, this paper presents an assessment of the research approach itself. Its contribution to the field is as a case study of an iterative, multi-method qualitative strategy bridging academic quality with practical, on-the-ground relevance.
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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 | 0.285 | 0.294 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".