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
The gender gap in science has been the focus of many analyses which have, for the most part, documented lower research productivity and citation impact for papers authored by female researchers. Given the rise of scholarly use of social media to disseminate scientific production and the healthy proportion of women on these sites, further investigation of potential gender disparities in social media metrics are warranted. Comparing event counts from Twitter, blogs, and news with citations, this study examines whether publications with male and female authors differ regarding their visibility on the social web and whether gender disparities can be observed in terms of social media metrics. Findings demonstrate increased gender parity using social media metrics than when considering scientific impact as measured by citations. It is acknowledged that this could be the results of the different impact communities, as the scientific community constituting the citing audience is more maledominated than the social media environment. The implications for the use of social media metrics as measures of scientific quality are discussed.
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 | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.054 | 0.250 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it