IN IT TOGETHER: ORGANIZATIONAL LEARNING THROUGH PARTICIPATION IN ENVIRONMENTAL ASSESSMENT
Bibliographic record
Abstract
This research explores opportunities for organizational learning through participation in environmental assessment (EA). The study examines information sharing, information interpretation, organizational memory and learning outcomes of organizations involved in two concurrent but geographically separate EAs: the Wuskwatim generation station and transmission lines projects (Manitoba) and the Snap Lake project (Northwest Territories). Primary data collection included semi-structured interviews with EA participants, and a review of documentation generated through each EA. Data were analyzed based on criteria derived from organizational learning literature. Findings indicate that organizations have a variety of structures that facilitate learning. Learning outcomes by state actors emphasized "single-loop learning", activities designed to improve performance within the existing EA process. Public actors, however, identified a wider range of outcomes centred on changing the EA process, termed "double-loop learning". These learning outcomes provide invaluable information about strengthening project specific EA, and provide insight into improving resource management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".