Forecasting labour markets in OECD countries : measuring and tackling mismatches
Bibliographic record
Abstract
Why forecast and for whom? some introductory remarks, Michael Neugart, Klaus Schomann occupations and skills in the United States - projection methods and results through 2008, Burt S. Barnow forecasting future skill needs in Canada, Douglas A. Smith labour market forecasting in Japan - methodology, main results and implications, Fujikazu Suzuki projections and institutions - the state of play in Britain, Robert M. Lindley a review of occupational employment forecasting for Ireland, Jerry J. Sexton beyond manpower planning - a labour market model for the Netherlands and its forecasts to 2006, Frank Chivers, Andries de Grip, Hans Heijke French occupational outlooks by 2010 - a quantitative approach based on the FLIP-FAP model, Agnes Topiol projections of qualifications and occupations in Austria - short-term approaches, macro perspective and emphasis on the supply side, Lorenz Lassnigg projecting labour market developments in Spain through 2010 - from massive unemployment to skill gaps and labour shortages?, Ferran Mane, Josep Oliver.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".