Needs and pathways for strengthening the contribution of qualitative methods toward more effective impact assessment practice
Bibliographic record
Abstract
Many jurisdictions are looking to next-generation impact assessment (IA) that includes sustainability considerations that extend beyond biophysical. Subjectivity is inherent in many of these additional impact considerations, and they are often not easily nor effectively quantified. Delivering effective IA within this broadening scope requires new, innovative, and rigorous applications of qualitative methods that enable meaningful inclusion of diverse knowledges, values, and information. While many qualitative methods are available for IA, there remains a significant opportunity to strengthen their contribution toward more effective IA practice. As such, we establish in this paper needs that must be addressed if qualitative methods are to meaningfully contribute to IA and pathways for acting on these needs. Relating findings from a survey, semi-structured interviews, and a world café, the paper specifically identifies six key needs for enhancing the effective use of qualitative methods in IA, and five pathways for addressing these needs that involve all IA actors. We conclude that there are deeply entrenched assumptions about qualitative methods and that shifting these views will be challenging and take time. Together, the identified needs and pathways provide a framework for action to improve the effectiveness of IA and should be considered in IA training and 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 | 0.641 | 0.602 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.028 | 0.038 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 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".