3 A Pan-Canadian Survey of Social Enterprises
Bibliographic record
Abstract
After much discussion, our research team was keen to examine the extent to which work integration social enterprises (WISEs) 1 in Canada responded to tenders issued by public, private, and nonprofit organizations, especially given the recent implementation of social-procurement policies in the public sphere.Our initial intention was to gain insight into the practice of social procurement from the perspective of both sellers and buyers.In November 2017, we embarked upon a nationwide survey of social enterprises, which was supplemented with semi-structured interviews to elaborate the survey results with further qualitative findings.We hoped the survey would give us an understanding of the engagement level of social enterprises in social procurement.We planned to use this as a point of contrast with a similar survey of the customers of social enterprises afterwards.Our expectation was that we would see a robust level of social enterprises participating in social procurement, implying a robust number of customers pursuing social procurement.Imagine our surprise when we found a low level of engagement and success in bidding.In this chapter, we outline the methodology and provide some background about the social enterprises that participated in our study.We then review the key findings from the survey and the follow-up interviews before discussing how these findings guided the selection of cases for a more in-depth understanding of social procurement and social purchasing in Canada.The findings from this first stage of
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".