We can't afford to do business this way: A study of the administrative burden resulting from funder accountability and compliance practices
Bibliographic record
Abstract
Many thanks to the Wellesley Institute for its confidence in backing this research, for its patience in waiting until the agencies had time to provide the data, and for its participation in helping to shape the final report. The quality of this study is due, in no small part, to the agencies that volunteered to inform the study design, provide the data, and respond to numerous requests for information. Their thoughtful participation was extremely helpful, bringing as it did, their different experiences with funders. Due to confidentiality concerns for both the agencies and their funders, the agencies cannot be identified, but you know who you are. Thank you so much. It was a pleasure to work with you. Thanks also to Rob Howarth of the Toronto Neighbourhoods Network, whose advice and participation helped make this study happen and helped to inform the results. Copies of this report and related documents can be found at www.wellesleyinstitute.com
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 | 0.056 | 0.244 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".