STUDY OF LIVING STANDARDS BEST PRACTICES IN LABOUR MARKET INFORMATION:
Bibliographic record
Abstract
The objective of this report for the LMI High-Level Advisory Panel is to provide advice on best practices in LMI and policy suggestions to improve the Canadian LMI system. Based on a thorough analysis, it presents 20 recommendations to improve the operation of LMI in Canada in the areas of LMI data, LMI analysis and forecasting, and LMI dissemination. For these recommendations to have traction, two conditions are needed. First, it is crucial that senior policy makers, that is those at the Deputy Minister and Ministerial level, recognize the important on an effective LMI system for a high-performance economy. Second, it is extremely important that jurisdictional issues do not become a barrier to the provision of high-quality LMI to the public. Résumé Le présent rapport est destiné au Groupe consultatif de haut niveau sur l‟IMT. Il renferme des conseils à propos des pratiques exemplaires en matière d‟IMT, ainsi que des suggestions stratégiques pour améliorer le système d‟IMT du Canada. Supporté par une analyse compréhensive, le rapport propose 20 recommandations en vue de l‟amélioration
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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 | 0.048 | 0.164 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".