Navigating Open Science as Early Career Feminist Researchers
Bibliographic record
Abstract
[Note that this paper is now published at Psychology of Women Quarterly in the Special Issue on Feminist Psychology and Open Science https://doi.org/10.1177/03616843211029255] Open science aims to improve the rigor, robustness, and reproducibility of psychological research. Despite resistance from some academics, the open science movement has been championed by some early career researchers, who have proposed innovative new tools and methods to promote and employ open research principles. Feminist early career researchers have much to contribute to this emerging way of doing research. However, they face unique barriers, which may prohibit their full engagement with the open science movement. We, ten feminist early career researchers in psychology, from a diverse range of academic and personal backgrounds, explore open science through a feminist lens, to consider how voice and power may be negotiated in unique ways for early career researchers. Taking a critical and intersectional approach, we discuss how feminist early career research may be complemented or challenged by shifts towards open science. We also propose how early career researchers can act as grassroots changemakers within the context of academic precarity. We identify ways in which open science can benefit from feminist epistemology and end with envisaging a future for feminist early career researchers who wish to engage with open science practices in their own research.
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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 | 0.103 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.032 | 0.082 |
| Scholarly communication | 0.029 | 0.033 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".