The role of labour market information in guiding educational and occupational choices
Bibliographic record
Abstract
Governments recognise that careers guidance, underpinned by accurate labour market information, can help learners make post-secondary education choices that match their interests, aptitudes and abilities, and lead to rewarding employment. For this reason, they have invested in building linked education/employment information systems and other information resources which are displayed on websites targeted to learners and their families. However, researchers and governments agree that these efforts are often ineffective in informing learners’ decisions – access to information is not sufficient to provide effective support to student choice. Drawing upon the insights of behavioural economics, this paper examines how learners access and use information, and what this implies for the design of public study and career choice websites that aim to effectively support student choice. The report also takes stock of the career guidance websites in use in the majority of OECD countries, and sets out to provide actionable advice for policy makers to guide the design of effective information policy levers that support student choice.
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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 | 0.026 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".