Bibliographic record
Abstract
(GLLAD) has just published a book entitled “The new forms of labour administration: Actors for development”, which examines labour administration in the present context of change and globalization, and describes and analyses 27 cases of innovative practice in this field. To write this book a number of specific studies were carried out during the course of 1999 and 2000, the publication of which may be of use to the constituents of the ILO. These texts describe different systems set up for specific purposes or in response to particular situations, such as the separation of jurisdiction between the federal and provincial governments of Canada, the importance of assessment in the United Kingdom and Finland’s new public administration. These four texts will be published separately. We wish to extend our sincere thanks to Mr. Jason Heyes, author of the text on the United Kingdom, Mr. Jean Bernier (Québec), Mr. Nigel Chippindale and Mr. John Dingwall (Ontario), authors of the text on Canada, and Mr. Markku Temmes and Ms. Helinä Melkas (revision), who were responsible for the publication on Finland. All publications were edited by Mr. Normand Lécuyer.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.416 | 0.267 |
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".