Place-Based Decision-Making: The Role of the Federal Government - Results from a Critical Conversation
Bibliographic record
Abstract
The Regulatory Governance Initiative (RGI) at Carleton University held a Critical Conversation on the role of the federal government in place-based decision-making on March 22, 2010. The objective of the event was to have an open debate amongst a diverse group of experts on the following question: From the perspective of sustainable development, what are the top three priorities to improve place-based analysis and decision-making and how can the federal government play a role? The workshop took the form of a Critical Conversation®: think-tanks that focus on challenging issues and aim to push the boundaries of current thinking on policy and regulation. This Critical Conversation identified three top priorities for improving place-based analysis and decision-making: the development of governance mechanisms, knowledge management and priority building, and the development of performance metrics.
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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 | 0.145 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.032 | 0.055 |
| Scholarly communication | 0.030 | 0.041 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.013 | 0.020 |
| 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".