Equity in Author Order: A Feminist Laboratory’s Approach
Bibliographic record
Abstract
Author order is crucial; it is the currency of academia. Within STEM disciplines, women and junior researchers--those who are the primary constituents of our lab-- consistently receive less credit for equal work. Our Civic Laboratory for Environmental Action Research (CLEAR) is a feminist marine science laboratory at Memorial University of Newfoundland, Canada. Recognizing that the stakes are high for CLEAR members, we have developed an approach to author order that emphasizes process and equity rather than system and equality. Our process is premised on: 1) deciding author order vy consensus; 2) valuing care work and other forms of labour that are usually left out of scientific value systems; and 3) taking intersectional social standing into account. Although CLEAR’s approach differs from others’, we take author order seriously as a compromised but dominant structure within science we must contend with. That is, rather than attempt to circumvent author order, we stay with the trouble. This article outlines this process.
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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 | 0.033 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.081 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".