Work Integration Social Enterprises (WISEs): Their Potential Contribution to Labour Market (Re-)Integration of At Risk Populations
Bibliographic record
Abstract
Canada’s nonprofit sector contributes significantly to the (re-) integration of economically at risk individuals into the workforce by providing employment related and social services. Over the last decade, more non-profit organizations (and a few for-profit organizations) have turned to a creative new strategy to help (re-)integrate highly disadvantaged populations into the workforce— the creation of social enterprise businesses that provide jobs for disadvantaged workers as well as training, placement and other supports. These jobs can be transitional, stops on the way to integration into the mainstream labour market, or stable, long-term alternatives to existing mainstream jobs. Restaurants, retail stores, courier services, cottage industries, and construction companies are common social enterprise businesses employing vulnerable populations in Canada (e.g. Elson & Hall, 2010). Recent surveys suggest that these organizations, known in Europe as WISEs (Work Integration Social Enterprises), are among the most common social enterprises in Canada’s emergent social enterprise sector (e.g. Elson & Hall, 2010; O’Connor et al, 2012; Flatt et al 2013). Keywords: CVSS, Centre for Voluntary Sector Studies, Working Paper Series,TRSM, Ted Rogers School of Management Citation:
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".