Beggin' for budgets : AIDS service organizations and the competition for funding in Alberta
Bibliographic record
Abstract
Public attention towards HIV/AIDS has shifted away from Western democracies in recent decades towards the global South. AIDS Service Organizations (ASOs) in the West, who were among the first in the world to respond to the epidemic, face increasing competition in relation to accessing nearly all of their traditional sources of funding. Within this thesis, the dynamics of competition for funding among ASOs operating in the province of Alberta is explored. Using Hilgartner and Bosk's (1988) public arenas model, I assume that the processes governing a social problem's rise and fall on the public agenda can be similarly applied to the study of non-profit funding. I introduce the concept of funding arenas to describe the location of financial resources available to ASOs in Alberta, and the social processes that influence successful competition therein. Seventeen interviews were conducted (N=17) with Executive Directors and other development staff from all thirteen ASOs operating in the province of Alberta. Content analyses of relevant ASO's funding documents are analyzed in conjunction with interview data. Two broad categorizations of five funding arenas are explored: government arenas and arenas of diverse competition. Cultural factors surrounding HIV/AIDS, including stigma, present various influences on the fund-seeking process in distinct funding arenas. In a critique of the public arenas model, I challenge the assumption that the objective conditions of a social problem bear little impact on the amount of attention it receives.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".