Identifying and promoting qualitative methods for impact assessment
Bibliographic record
Abstract
Qualitative methods for impact assessment (IA) represent a broad spectrum of approaches that are important for realising effective IA practice. The purpose of this paper is to identify and promote qualitative methods that are available for use in contemporary and future (next-generation) IA processes. From an extensive literature review, an international survey (145 responses), expert interviews (48 interviewees), and a workshop attended by 27 IA practitioners, 17 qualitative method categories were identified. These were further subdivided into three classes: conventional qualitative methods, highly participatory methods, and mixed methods. Each method is described, and an indication given of how each can be used in IA practice, including the specific stage of the IA process to which they might be applied. Whilst this paper seeks to stimulate practitioners to apply qualitative methods to enrich IA practices, the research also identifies a lack of expertise with social science methods as a significant barrier to the effective use of qualitative methods in IA practice.
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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 | 0.338 | 0.332 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".