Bibliographic record
Abstract
Many Western European countries have dramatically ramped up spending on integration in the hope it will help the large numbers of recently arrived refugees find work and settle into their new societies. But very little is known about how best to target these investments. Governments have little hard evidence of what constitutes value for money in integration, in part because investments rarely pay off right away; it can take years or even generations for their full effects to be felt. There is also a dearth of high-quality evaluation to suggest which types of interventions—from subsidised work experience to training programmes—work best. Very few evaluations of integration policies can prove that the outcomes observed are the result of the intervention, and even most high-quality evaluations only look at the short-term effects of policies and programmes. This report outlines ways policymakers can use a tool often employed by economists—cost-benefit analysis—to calculate the broader social value of their labour-market integration investments and to improve the quality of evidence in this field. Established methods from policy areas such as health and criminal justice are used where—like integration—spending may only pay off over a long timeframe. Such methods allow researchers to model the likely long-term outcomes of interventions, even in the absence of robust evaluation evidence on such interventions, or where initiatives are simply brand new. In other words, it allows decisionmakers to say: if a training programme has its desired effect, for every X euros of investment the programme is expected to produce a Y euro return over a 30-year time period.
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 | 0.293 | 0.585 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.029 | 0.061 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.042 | 0.065 |
| Insufficient payload (model declined to judge) | 0.047 | 0.016 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".