The Ethics of Private Funding for Graduate Students in the Social Sciences, Arts, and Humanities
Bibliographic record
Abstract
This article offers a review of the strategic opportunities and ethical risks involved in the institutional pursuit of private funding for graduate students in the social sciences, arts, and humanities (SSAH) fields. There is little existing research about private funding for SSAH research, and this article seeks to address this gap. In addition to reviewing relevant literature about trends in the privatization of higher education, shifting funding priorities, and the ethics of private funding, we offer a set of guiding principles for developing a private funding policy in SSAH fields. We also illustrate relevant considerations and concerns using the example of a private funding policy for graduate student within a faculty of education in a public university in Canada. The discussions in this paper are relevant to public higher education institutions questioning how they can ensure the integrity and sustainability of their research activities in a changing funding environment.
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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 | 0.109 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.050 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".