GeoLatinas beyond earth sciences: for an equitable, inclusive, and diverse planetary and space science
Bibliographic record
Abstract
GeoLatinas in Space is an initiative that fosters scientific literacy in an inclusive environment. For decades access to space-related formation has been precluded to social advantage groups. Minorities have faced low visibility of role models in leadership positions, language barriers, lack of access to resources and information, and ultimately non-inclusive working spaces, resulting in an even more challenging environment. In light of current and historical social challenges that minorities face, GeoLatinas’ visionary purpose offers a platform that aims to empower Latinas in Earth and Planetary sciences. Our community intends to create an inclusive, safe space for scientists from different backgrounds to converge. The new space race is growing exponentially, and occupations in space are becoming more and more relevant. The technology revolution is already here, but it is still centered and constrained by linguistic restrictions. As the new space race gets underway, a need for scientifically competent individuals from other fields will also arise. To promote literacy and communication in planetary sciences, GeoLatinas in Space has established a community that encourages information sharing, makes it approachable, and assures that it is evenly circulated in multiple languages. By providing and expanding accessibility to space literacy content and encouraging the creation of professional profiles dedicated to space projects and the cosmos, our goal and efforts are focused on closing knowledge gaps in developing nations, particularly Latin America. By showing that space jobs are feasible today and accessible to those who are interested in pursuing them, we engage a broader audience and work to inspire younger generations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".