To What Extent is the Canadian Charitable Sector a Politically Expressive Sector?
Bibliographic record
Abstract
This thesis asks: to what extent is Canada's nonprofit sector a politicallyexpressive sector?To address this question, I propose three sub-questions, with each framing a study.These questions are addressed using data sets covering the provinces of Ontario and Alberta from 2003-2017.The combined results show that the charitable sector is indeed an outlet for aggregating and expressing collective identities, but that the degree to which this happens depends on the political context.In the first study, the question is: do private family foundations express these identities through their granting?Findings show that there are geographic differences in how politically aligned foundations grant and that politically aligned family foundations are much more likely to give to religious missionary charities while non-political foundations are much more likely to give to mainline Christian congregations, while both groups of foundations are less likely to give post-secondary institutions.When looking at charity revenue from government in the second study, the research question is: how does government funding for charities change when the party in power changes?Or, more specifically, when electoral district representatives and governments change, which nonprofits gain new funding and which nonprofits lose funding and to what extent do these changes reflect the interests of the new government?Ontario, which has a competitive and diverse political environment, experiences more of the effects suggested by the literature: changes in funding reflect partisan identity as governments and local iii representatives change.In Alberta-where the political culture is quite stable, less competitive, and where politics tends to be dominated by ideologically conservative within-party concerns rather than between-party competitionthere is less change in funding to charities around elections and changes in government.In the third study the question is: are contributions to political parties and charities positively or negatively correlated expenditures?The results show they are positively correlated and that the factors associated with higher likelihoods of charitable donations also correlate with higher likelihoods of political contributions.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".