Welfare to Work: Creating a Community Where all Can Work
Bibliographic record
Abstract
Finding the right mix of policy options to ensure that all members of society who are able have the opportunity to work is a key challenge facing Canadian governments. In Manitoba, the challenge is complicated by an aging workforce, significant barriers to labour force participation facing the rapidly increasing aboriginal population, skills shortages, and low wages in many sectors. Manitoba’s current government rejected the prevailing philosophy that tax cuts and workfare programs would reduce the number of people on assistance. Instead, the Government adopted a balanced approach, restoring key services, strengthening communities, expanding education opportunities, and reducing both the debt and taxes in a sustainable manner. The approach focused first on getting the economic and fiscal fundamentals right, and then on finding the right mix of policies and programs to help people find permanent, meaningful work. Manitoba’s unemployment rate remains the lowest or second-lowest in Canada, while its youth unemployment rate is well below the national rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.022 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".