Evaluating the cross-disciplinary utility of anonymizing applications for scientific equipment in the Australian research sector
Bibliographic record
Abstract
Abstract Anonymizing applications for research resources has been demonstrated to reduce bias against women, early career researchers and other marginalized researchers, specifically for applications to use scientific equipment in planetary and space science research. We conducted a nationwide trial in Australia to evaluate the cross-disciplinary impacts of anonymizing applications for use of scientific equipment. The twofold purpose of the study was to examine whether disparities existed–and if so, to quantify their size and direction–and to evaluate how anonymizing applications would impact application outcomes, based on the gender and career seniority of the lead researcher. The trial involved applications to four Australian research entities managing access to national scientific facilities. Entity-specific modelling was carried out, followed by a meta-analysis to assess overall effects. Our evaluation reveals a noteworthy absence of gender and career seniority disparities in application outcomes before anonymization across most entities, with one exception where women-led applications received more resources in a specific program. The introduction of anonymization led to improved success rates for early-career researchers, while generally maintaining existing gender parity, with one entity showing improved success rates for women-led applications. The implications extend beyond funding outcomes, which represent only one piece of the puzzle contributing to inequity in STEM research. By enhancing success rates for early career researchers, anonymization may create a ripple effect by diversifying the research pool, and supporting, retaining and advancing researchers facing barriers in STEM research. Future research examining cultural, racial, and other biases is key to refining equity efforts.
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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 | 0.275 | 0.459 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".