Understanding the Funding Game: The TextualCoordination of Civil Sector Work
Bibliographic record
Abstract
Abstract. This paper investigates how people’s work for non-profit organizations, charities, grassroots collectives, and social justice organizations is organized by official funding processes. In my analysis, I attend to the different kinds of text-based knowledge that coordinate people’s work across the civil sector. Engaging in discussions with participants about their work, I discover how an individual’s ordinary documentary activities are articulated to institutional relations of accountability. Attending to text-driven accountability practices — practices increasingly taken up to justify and carry out all kinds of work in the civil sector — I investigate the ideological organization of people’s work via the policy documents and textual application procedures of the Revenue Canada tax act with regard to Charitable Status and the Ontario Trillium Foundation funding application process. Résumé. Cette communication s’intéresse aux personnes qui travaillent dans le milieu qui regroupe les organismes à but non-lucratif, les oeuvres de bienfaisance, les collectifs communautaires et les organismes en justice sociale, du point de vue de l’impact exercé sur leur travail par le processus des demandes de financement officiel. Dans mon analyse, je m’attarde à la manière par laquelle une diversité de savoirs textuels vient coordonner ce travail dans l’ensemble du secteur civil. Au moyen de discussions avec participants au sujet de leur travail, je découvre comment les activités normales de documentation qu’effectuent ces individus sont liées à des relations institutionnelles d’obligation de rendre compte. En m’attardant à la primauté du texte vis-à-vis ces comportements d’obligation de rendre compte — comportements qui de plus en plus servent de justification à une gamme importante de fonctions dans le secteur civil — j’enquête sur l’organisation idéologique du travail en question via les documents de politique et de procédure d’application textuelle de la loi de Revenu Canada portant sur le statut caritatif et sur les démarches de demande de soutiel de la Fondation Trillium de l’ Ontario.
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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".