1 Literature Review
Bibliographic record
Abstract
Overview of Non-proft Social Enterprises, Social Procurement, and Social Purchasing Social enterprises are organizations operating in the market that have a social mission.Some maintain that social enterprises represent a shift in the economy from one that has a pure profit orientation to one that includes some social consideration.Others argue that social enterprises are the latest turn in the neoliberal economy where governments offload responsibility for the public to the private and non-profit sectors (Ganz et al., 2018; Spicer et al., 2019).Both sides, however, would agree that the impact of social enterprises goes well beyond the traditional bottom line.This is particularly true of non-profit social enterprises, which reinvest their profits into their social mission rather than paying them out to shareholders.One type of non-profit social enterprise -Work Integration Social Enterprises (WISEs) -aims to integrate marginalized social groups into the mainstream workforce.While WISEs that employ or train members of marginalized social groups have often relied on support from their parent organizations, government funding, charitable foundations, and social purchasing at the community level, increasing policy interest in social procurement offers a new revenue opportunity for social enterprises.The experiences of WISEs engaging in both social procurement and social purchasing have not been studied until now.To set the context for such a study, the first section of this book provides an overview of social enterprises, social procurement, and social purchasing.Chapter 1 presents a literature review providing depth and breadth to these central concepts.It describes the changing marketplace before focusing on social enterprises, particularly WISEs.It also delves into how existing research literature defines two forms of buying social: social purchasing and social procurement.Chapter 2 explores the policies around buying for social value on behalf of governments in Canada.In particular, it focuses on different
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.020 |
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".