Evaluating equity in scholarly publishing
Bibliographic record
Abstract
Abstract The Workplace Equity Project (WE) conducted a global survey in 2018 to map the parameters that define the scholarly publishing landscape, understand the drivers for change, and recommend solutions for delivering improved outcomes. Results find that there are imbalances in diversity inherent in the workforce (96% with a Bachelor's degree or higher, 76% female, 83% heterosexual, 81% White, and 89% report no disabilities) and that outcomes diverge for respondents based on their gender and ethnicity, highlighting distinct inequities in the workplace. The leadership profile is more male (33% vs. 21%), and more White (91% vs. 81%) than the sample as a whole. Moreover, those respondents with characteristics more reflective of the leadership profile are more likely to report that they believe they are fairly compensated, recognized by the leadership, and have fair opportunities for advancement. The article recommends improvements to current practice in recruitment, sponsorship and advocacy, and staff retention to drive change towards creating a more equitable, inclusive workforce. Key points Senior leaders in scholarly publishing are more likely to be older, White males with master's degrees. The chances of attaining a senior position in scholarly publishing are higher for White males with no tertiary‐level qualifications than for Black females with postgraduate degrees. Most demographic groups (with the exception of the Black cohort) recognize their own challenges but are blind to the bias experienced by other groups. Direct line management practice defines individual experience irrespective of organizational policy. White and Asian males are more likely to be satisfied with career prospects and rewards than other groups. White women are more likely to be satisfied with their current positions than other groups. Job satisfaction in scholarly publishing varies according to ethnicity and gender, and companies have a duty to be more inclusive and diverse in their employment.
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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 | 0.100 | 0.243 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".