On the individual and organizational capacities supporting impact assessment: the case of the Yukon Environmental and Socio-Economic Assessment Board
Bibliographic record
Abstract
Impact assessment (IA) processes rely on the ability of assessment boards and their assessors to gather, synthesize, and interpret knowledge from a variety of sources, making IA a knowledge-based activity. IA boards in northern Canada operate in a context that prioritizes pluralism, where Indigenous knowledge is a key element of decision-making and the ability of practitioners to interact with knowledge—research capacity—affects process effectiveness, credibility, and legitimacy. Drawing on common principles from existing research capacity frameworks, we identify the dimensions of capacity most relevant to more fully realizing inclusive impact assessment processes. We then examine the Yukon Environmental and Socio-Economic Assessment Board (YESAB) as a specialized environmental governance organization with assessors whose research capacity directly impacts process outcomes. Results show that while common dimensions across knowledge-based disciplines, such as sufficient resources (e.g., financial support), are often addressed in the YESAB context, others unique to IA, like contextual understanding, require further examination. The interaction between individual and organizational research capacity is a complex balance between investing in individuals and investing in organizational supports. The proposed framework facilitates multi-scalar supports for individual assessors and assessment bodies alike to navigate balancing technical and value-driven knowledge in assessments.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".