Conceptual Framework for an Optimal Labour Market Information System: Final Report
Bibliographic record
Abstract
The importance of labour market information (LMI) to the efficient functioning of product and input markets has increased with the expanding globalization of economic activity.Markets transcend national boundaries and operate continuously.High-quality, easily accessible LMI is a fundamental feature of a well-oiled competitive labour market, and LMI can improve both short-and long-term matches of labour supply and demand to ensure that individuals build and renew the skill sets required in the dynamic marketplace.This study provides the framework for an optimal labour market information system.By optimal we mean best given the available resources.Our aim is to provide a practical model for Human Resource and Social Development Canada (HRSDC) as it improves and refines its LMI activities in the real world of public policy with limited budgets.Canada is widely recognized as a world leader in LMI development and delivery.Many features of an optimal LMI system already exist in Canada.Indeed, many principles were initially developed by Canadians and have been assimilated in other countries.Nonetheless, dramatic and rapid changes in markets suggest that occasional reviews of LMI are beneficial to keep in step with the changing information requirements and methodologies.Periodic reviews may identify new gaps emerging from change, and suggest changes so that LMI systems remain useful and relevant to job seekers, employers, and public policy makers.This study also examines the critical need for governmental intervention to build and maintain an optimal LMI system.Theory suggests that markets themselves might generate information sufficient for effective decisions, but the complexities of the market make it nearly impossible for all or even the most relevant information to be generated and made available through normal market transactions.Government must play a major role to ensure there is a public LMI system, which produces reliable and relevant information, and that LMI is disseminated in user friendly ways to increase the likelihood of improving the efficiency of
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".