Building Flexibility and Accountability Into Local Employment Services: Synthesis of OECD Studies in Belgium, Canada, Denmark and the Netherlands
Bibliographic record
Abstract
Human resources and skills are becoming increasingly important to economic development. In the context of the economic downturn, challenges such as high youth unemployment call for a collaborative approach between local employment officials, educational institutions and wider social and economic partners. But do local labour market offices have sufficient flexibility in the implementation of their policies and programmes to contribute effectively to local strategies? If local labour market offices are to be given more flexibility, how can this be reconciled with the need for accountability and the achievement of national policy goals? Building Flexibility and Accountability into Local Employment Services examines how four different countries have responded to the challenge of rewarding local employment offices more flexibility while retaining accountability: Belgium, Canada, Denmark and the Netherlands. It provides policy recommendations for policy makers at all levels, which were discussed at a high level international OECD conference in Aarhus, Denmark in April 2011. See also the following working papers and country reports from the project (2011/11, 2011/12, 2011/13).
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.038 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".