KNOWLEDGE MANAGEMENT IN ENVIRONMENTAL IMPACT ASSESSMENT AGENCIES: A STUDY IN QUÉBEC, CANADA
Bibliographic record
Abstract
Environmental impact assessment (EIA) is a knowledge intensive activity that benefits from a highly structured approach to knowledge management (KM). In a survey of KM initiatives in two Québec government agencies, the Environmental Assessment Department and the Environment Public Hearings Bureau, knowledge repositories were mapped and officers were invited to reply to a questionnaire enquiring about the knowledge repositories' usefulness. Their perception about knowledge creation within each agency was assessed. Three drivers were identified that have steered the implementation of KM initiatives: (i) successive managers' understandings that EIA does create knowledge; (ii) a concern with consistency and reproducibility of recommendations; (iii) improving agencies' efficiency, alongside one additional incentive: curbing the deleterious effects of staff turnover. So far an unmet challenge is making sense of a great deal of data and information obtained in the follow-up phase of the EIA process and transforming it into knowledge used to improve both efficiency and effectiveness of an agency's work.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".