Legislation and Lifelong Learning in Canada: Inconsistencies in Implementation
Bibliographic record
Abstract
Governments in Canada at both the federal and provincial levels indicate that they support the policy of enhancing education to increase the 'intellectual capital' of the Canadian workforce as they believe that this would in turn, improve the economy. The lifelong learning agenda is at the heart of this effort. However, it is argued in this article that a review of government policy as expressed in legislation reveals inconsistencies between rhetorical and actual statutory support for the life- long learning agenda. The absence of the protection and sense of permanence that legislation provides to policy implementation means that any actions taken or programs created may be easily changed, ignored or eliminated with little public scrutiny or debate. Further, this absence establishes a lack of firm ongoing political commitment to achieving the learning agenda.
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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 | 0.072 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.032 |
| Science and technology studies | 0.022 | 0.017 |
| Scholarly communication | 0.023 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| 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".