Understanding the role qualitative methods can play in next generation impact assessment
Bibliographic record
Abstract
Since its inception, impact assessment (IA) has been perceived by many to be a largely technical, quantitative exercise. However, as jurisdictions shift towards a more sustainability-oriented IA that accounts for a wider range of social, cultural, economic, health and well-being, and equity implications of proposed projects and strategic initiatives, values and subjectivity come more to the fore. Making predictions now needs innovative, and rigorous applications of qualitative methods that enable meaningful inclusion of diverse knowledges, values, and information sources, whilst at the same time giving confidence to decision makers and other stakeholders about the evidence base. Adopting such qualitative methods in practice is hindered by a lack of clarity of the role of qualitative methods in the delivery of sustainability-oriented IA. Guided by findings from a thematic analysis of primary data gathered through an international survey supplemented by semi-structured interviews and a workshop, the novel contribution of this paper is to clarify how and why qualitative methods can best contribute to the effective delivery of next generation IA. • Sustainability-oriented IA incorporates values and subjectivity. • Qualitative methods are needed to embrace subjectivity. • Lack of understanding of role of qualitative methods threatens application. • Five essential roles of qualitative methods in IA were identified.
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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 | 0.414 | 0.352 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".