Policy advocacy experiences of Saskatchewan nonprofit organizations: Caught between rocks and hard places with multiple constituents?
Bibliographic record
Abstract
In Canada, nonprofit, social service, community‐based organizations (CBOs) play an important role in advocating for social policies intended to positively influence people's lives. This article explores the challenges and opportunities that exist within the dynamic relations among social service CBOs, the marginalized communities they serve, and governments, as these CBOs attempt to influence social policy development. Qualitative data were collected from CBOs from 18 communities throughout the province of Saskatchewan and these data show how CBOs find themselves having to juggle and negotiate with multiple constituents and their myriad perspectives, often resulting in feelings of being caught between rocks and hard places. Nonetheless, analysis of the data also indicates these CBOs manoeuvre themselves around these precarious circumstances to find soft spots for progressive change. In my conclusion, I focus on the multiple constituent perspectives with which CBOs must contend, the potential salience of constituents in advocacy theory, the possible role of geographic and spatial variables in advocacy theory, iterative decision‐making loops that may better characterize advocacy than decisions made in serial fashion, and finally, advocacy chill and advocacy warmth.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.047 | 0.019 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| 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".