Canada's Technology-Centric Workfare On-Line Employment Services
Bibliographic record
Abstract
Canada's knowledge society labour policy is reflected in the federal government's re-designing and re-scaling of the welfare state. Part of this re-designing has been connecting labour market and social policy - that is combining the ‘workfare model' with the ‘social investment model' of lifelong learning. These changes are reflected in the 1995 Employment Insurance Act (EIA) with an emphasis on the re-commodification of labour and the importance of active reemployment. Under this policy, all working age individuals' have a duty and obligation to work whether they receive social assistance, employment insurance benefits or a disability pension. In addition to tightening employment insurance eligibility requirements the Act has also shortened benefit periods. The Act is also accompanied by a minimal safety net that offers short-term employment public programs, available on a competitive basis to community non-government organizations, ostensibly to assist those who have been previously excluded from the workforce. An emphasis has been placed on life-long learning and skills and training for those planning on entering or re-entering the workforce. More recently the federal government has launched, what it refers to as a citizen-centric on-line employment services. Client services include: the on-line data Job Bank, a web-based network of Canadian job postings; the Electronic Labour Exchange (ELE), that matches job seekers with businesses; on-line Employment Insurance (EI), reporting for those out-of work and receiving benefits, as well as the on-line Web Record of Employment (ROE).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.008 |
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".