Does Simulating Financial Equality Reduce the Political Donations Gender Gap?
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
A variety of research has found strong evidence that men are more likely to donate to political parties and candidates relative to women. Yet studies of other kinds of political participation have observed a shrinking gender gap. What explains this variation? One possible explanation comes from several studies that report women are more likely to donate to nonpolitical groups when told they have been entered into a monetary draw. Does simulating resource equality also reduce the political donations gender gap? To answer this question, we analyse original Canadian data from two surveys that asked participants how much they would give to a federal (survey 1)/provincial (survey 2) political party if they were given CDN$100. Contrary to our expectations, we find no gender gap at the federal level and a positive gap favouring women at the provincial level.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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