Evaluating Policy Outcomes: Federal Economic Development Programs in Atlantic Canada
Bibliographic record
Abstract
Abstract: The Atlantic Canada Opportunities Agency (ACOA) was established in 1987 as the federal government agent for economic development in Atlantic Canada. This article describes the Agency’s “corporate” approach to provide credible, quantitative estimates of the long-term policy outcomes of total Agency activity. The focus of evaluation activity has been to determine the contribution of ACOA as a whole to its legislated mandate to enhance the growth of earned incomes and employment opportunities in Atlantic Canada. To provide reliable impact estimates for senior management, ministers, parliamentarians, stakeholders and taxpayers, the Agency has used multiple lines of evidence, both qualitative (client-user surveys and independent verifications) and quantitative, such as economic statistics from multiple sources, econometric modelling, and time-trend analysis versus a comparison group (ACOA clients versus all Atlantic small- and medium-sized enterprises), to measure policy outcomes. This methodological approach is detailed and the findings with respect to broad macro-economic indicators such as employment creation, earned income, tax revenues, and the unemployment rate are presented.
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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 | 0.040 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".