What Works for Whom in Public Employment Policy?
Bibliographic record
Abstract
What works for whom in public employment policy? 1 participation were reported.Estimates of impacts on EI receipt were rarely statistically significant. Employment Assistance Services (EAS).These programs are generally short and relatively low-cost.Often EAS are combined in action plans with other interventions.Because of this complexity, evaluations of EAS have tended to focus on the group of "EAS-only" claimants.According to the British Columbia data, these represent perhaps 65 percent of individuals who received any EAS-related services, but a much smaller fraction of total EAS services provided (because those with an Employment Benefits intervention tend to have more EAS interventions than do members of the EAS-only group).The extent to which the EAS-only group is representative of all EAS participants has not been explicitly addressed in the evaluations, but on a priori grounds it seems plausible that this group might have more successful employment experiences than the other EAS participants.Results for active claimants were generally not statistically significant for employment and earnings, with the exception of one jurisdiction where an earnings increase was estimated.In part, this may have resulted from the difficulty of detecting such impacts given the small sample sizes available in the evaluations.For EI weeks, five out of eight evaluations generated statistically significant impacts; these included both positive and negative results.Given the mixed results, no overall conclusions can be drawn about the impact of EAS-only in the EBSM context.EAS participants did report strong levels of program satisfaction, job readiness, and interest in further training. Lessons Learned and Knowledge GapsWe list the lessons learned and the remaining gaps in knowledge about effects of EBSM operated under LMDA in the provinces and territories.Likely gaps concern effects by participant characteristics, program features, labour market conditions, and bundling or sequencing of services. Skills Development.Sample size restrictions generally prevented the evaluations from estimating effects of SD separately for subgroups of participants.A few of the evaluations did report that women had somewhat more favorable overall impacts on employment and earnings than men, though these results were generally not reported separately for SD participants only.In the evaluations that were able to estimate gender-specific impacts for SD participants only, gains for men often exceeded those for women.Hence, the EBSM results may not precisely mirror the international finding that women are more likely to benefit from training than men.Estimated impacts of SD on former claimants were more variable than were SD impact estimates for active claimants.This larger variance in results may in part be explained by the difficulties that some of the evaluations had in identifying a proper comparison group for former claimants, some of whom had been out of the labour force for some time.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".