An Overview of the Summative Evaluations of Employment Benefits and Support Measures Under the Labour Market Development Agreements
Bibliographic record
Abstract
benefits to workers who have lost their jobs. Part II provides employment benefits and support measures (EBSM) to help current and former EI clients return to work. Although Part I benefits are administered by the federal government, the 1996 Act gave provincial and territorial governments significant roles in operating the Part II programs. Under the Labour Market Development Agreements (LMDA) mandated by the Act such responsibility can be either fully transferred to the provinces and territories, or the programs can be “co-managed ” and operate under shared responsibilities. The 1996 Act also required that programs operated under the LMDAs be subject to formal evaluations over the ensuing decade. Many of these evaluations have now been completed. Typically the evaluations have been undertaken in two stages. The first, “formative ” evaluations looked at how the LMDAs were implemented and examined in detail some of the programs being operated under them. A second round of “summative” evaluations followed. These focused on measuring the impacts that the programs had. To date seven summative evaluations have been completed for Alberta, British Columbia, Newfoundland, Nunavut1, Ontario, Quebec, and Saskatchewan. The purpose of this
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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 | 0.076 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".