Employment policies and multilevel governance
Bibliographic record
Abstract
Acknowledgements. Notes on the Authors. Introduction. Juan Pablo Landa & Brian Langille Part I The Framework Context of Europeanization of National Employment and Social Inclusion Policies 1. The Impact of Globalization on Employment and Social Inclusion Policies: Experiences and Proposals in Individual European Countries - Jean-Michel Servais 2. Looking at the EES in Search of Effectiveness and Efficiency of National Employment Policies and Social Protection Systems - Juan Pablo Landa Zapirain & Edurne Terradillos Ormaetxea 3. EES and European Social Inclusion Policy: Employment as a Means of Social Inclusion in an International Human Rights Perspective - Aranzazu FernaA ndez Urrutia & Nuria Pumar BeltraA n 4. The OMC as Decentralization of Regulations and Case Law: A Gender Mainstreaming Perspective - Julia LoA pez LoA pez Part II Multilevel Governance Experiences on Employment Policies in a Cross-National Perspective 5. The Reform of the Labour Market and of the Social Benefits for Unemployment in Germany - Maximilian Fuchs 6. The Reform of the Public Employment Service in France: Modernization and New Governance Issues - Philippe Auvergnon & Philippe Martin 7. Vocational Education Policies in the Process of Multilevel Governance: A French Perspective - Thierry Berthet & Pierre Iriart 8. Features and Limits of the Regionalization of Social and Employment Policies in Italy - Giancarlo Ricci 9. Centralization and Decentralization within the Spanish Model of Social Federalism: The Examples of Social Assistance and Employment Policies - Antonio Baylos, Jaime Cabeza & Maria Jose Romero 10. The Jobseeker's Allowance: A British Perspective on Job Activation - Jo Carby-Hall 11. Who Governs Labour Market Policy in Canada? - Brian A. Langille
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 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".