Finding Your Crowd: The Role of Government Level and Charity Type in Revenue Crowd-Out
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The literature on the relationship between government funding and private donations finds evidence of both crowd-out (a reduction in private donations due to the receipt of government funding) and crowd-in (increased donations rather than a reduction). This study uses organizational-level data and information regarding funding from multiple levels of government in Canada to provide an important contribution to the literature, which has traditionally relied upon aggregated measures of government funding. Results from dynamic panel estimations suggest that effects vary by type of charity and level of government funding source, with some subsectors displaying crowd-in, some crowd-out, and some both phenomenon depending on the level of government providing funding. These findings encourage more research into the context and variation involved in crowd-out, raising practical and theoretical questions about whether a uniform effect across subsectors or level of government funding should be expected.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it