Who are the acknowledgees? An analysis of gender and academic status
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 Acknowledgements found in scholarly papers allow for credit attribution of nonauthor contributors. As such, they are associated with a different kind of recognition than authorship. While several studies have shown that social factors affect authorship and citation practices, few analyses have been performed on acknowledgements. Based on 878,250 acknowledgees mentioned in 291,167 papers published between 2015 and 2017, this study analyzes the gender and academic status of individuals named in the acknowledgements of scientific papers. Our results show that gender disparities generally found in authorship can be extended to acknowledgements, and that women are even more underrepresented in acknowledgements section than in authors’ lists. Our findings also show that women acknowledge proportionally more women than men do. Regarding academic status, our results show that acknowledgees who have already published tend to have a higher position in the academic hierarchy compared with all Web of Science (WoS) authors. Taken together, these findings suggest that acknowledgement practices might be associated with academic status and gender.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| 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