Space4Women Landmark Study on Gender Equality in the Global Space Sector
Bibliographic record
Abstract
Gender equality has a transformative impact on everything from individuals to institutions and innovations in the space sector. Despite this, the space sector has many data gaps when it comes to gender equality, hampering our ability to know what to do, and how to do it, and impacting individuals’ experiences in the sector. Gender inequality in the sector has broader implications for talent retention, recruitment, and the sustainable uses of outer space. This study builds on the Phase 1 Landmark Study on Gender Equality in the Global Space Sector and the UNOOSA Space4Women Expert Meetings in the Republic of Korea, Canada, and Kenya. This report comprises two parts, launching the UNOOSA Phase 2 research into gender equality in the space sector. The first part of this report focuses on women’s experiences in the sector, while the second part provides information on gender representation in private space organizations and examines policies or interventions that advance gender equality. The purpose of both is to drive transparency, action, and progress towards equality and inclusion in humanity’s ambitions in space. Not only is this critical to the Space2030 Agenda and the Sustainable Development Goals but it is also a moral and strategic necessity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".