Diversity and Identity in the Non‐profit Sector: Lessons from LGBT Organizing in Toronto
Bibliographic record
Abstract
Abstract The objective of this paper is to explore the ways in which diversity is taken into account in the conceptualization, definition and role of the voluntary sector as well as policy debates around the recasting of relations between the state and the voluntary sectors. The paper is based on a study of voluntary sector organizing among lesbian, gay, bisexual and transgender (LGBT) citizens in the city of Toronto. It presents an overview of LGBT voluntary sector organizing in the city, demonstrating the rich network of non‐profit organizations that serve the LGBT community in the city of Toronto, Canada's largest city. The paper argues that the dominant cross‐national and cross‐time definitions of the voluntary sector do not account for some of the specific features of LGBT organizing and result in the marginalization of such organizing from the very concept of the voluntary sector. The paper discusses the implications of this mapping for policy discussions of the state–voluntary sector relationship. Drawing on the Canadian experience of government consultation with voluntary sector organizations, the paper demonstrates that such initiatives define certain forms of diversity in voluntary sector organizing out of the policy‐making process. Traditional policy‐making around voluntary sector issues is organized in ways that exclude urban and local identity‐based organizing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.036 | 0.022 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".