Reviewing the 2018 OECD Jobs Strategy – anything new under the sun?
Bibliographic record
Abstract
This article analyses the documents released by the OECD at the end of May 2018 launching a revised Jobs Strategy. The analysis frames the new initiative in the context of the two previous Jobs Strategies of 1994 and 2006. We pose the general question of whether continuity or discontinuity is the prevailing theme, along with a specific question concerning the significance of new themes of job quality and inclusive growth, given the documents’ simultaneous endorsement of the older theme of flexibility-enhancing policies to ensure a labour market functioning without rigidities. Flexibility had been the central feature of previous Jobs Strategies, although there was also a nod in the direction of an alternative route to better employment performance in the 2006 revision. The 2018 documents assert that the new strategy represents a significant departure from the previous iterations. This article provides a critical evaluation of this claim.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".