Learning Oriented Impact Assessment Under the Proposed Federal Impact Assessment Act (IAA)
Bibliographic record
Abstract
Note: This post was prepared jointly with my colleague, Professor John Sinclair at the University of Manitoba. \nRather than merely treat assessments as hoops for proponents to jump through in order to gain project approval, it is now more commonly recognized that impact assessments should be centered on learning. To achieve this, the potential for learning by all participants must be recognized throughout the assessment process from the earliest pre-planning phases through to monitoring of effects and outcomes. In fact, we contend that the current crisis in federal EA in Canada is caused in part by the lack of integration of learning into EA. This suggests that the potential benefits of learning need to be recognized throughout all of the typical stages of a strategic, regional or project assessment. Four aspects of assessment processes are particularly important to fostering a learning orientation. They are public participation, knowledge development, monitoring of effects and regime evolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".