Situating the Nonprofit Industrial Complex
Bibliographic record
Abstract
This article centers on the nonprofit landscape in Vancouver, Canada, a city that occupies the territories of the xʷməθkʷəy̓əm (Musqueam), sḵwx̱wú7mesh (Squamish), and səlilwətaɬ (Tsleil-Waututh) nations, which have never been ceded to the colonial occupation of Canada. Vancouver has a competitive nonprofit field, with an estimated 1600+ nonprofits operating within city limits. This descriptive review starts by defining what a nonprofit industrial complex (NPIC) is, then outlines an abbreviated history of the nonprofit sector on the aforementioned lands. The article then explores issues related to colonialism, anti-poor legislation, neoliberal governance, the fusing of the public and private sectors, and the bureaucratization of social movements and care work as mechanisms to uphold the status quo social order and organization of power. Focusing on under-examined issues related to the business imperatives of nonprofit organizations in the sectors of housing, health and social services, community policing, and research, this work challenges the positive default framing of nonprofits and charities. Instead, we contend that Vancouver’s NPIC allows the government and the wealthy to shirk responsibility for deepening health and social inequities, while shaping nonprofits’ revenue-generating objectives and weakening their accountability to the community.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.026 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".