Bibliographic record
Abstract
This is an invited presentation of an Oregon State University DEI-related seminar series during Winter Term 2022 called RESET (Radical Earth Science Equity Transformations) with the intent of helping the Oregon State University College of Earth, Ocean, and Atmospheric Sciences (CEOAS) community to become more culturally competent, empathetic, and self-aware. Courtesy CEOAS Professor of Geography and Oceanography Dawn Wright reflects on her experience as a woman of color within the geosciences, shares the story of how she got into GIS as well as advice on navigating through the broader GIS/spatial data science world of today, share some formative experiences and insights since moving full-time to Esri, including work-life balance (with LEGO bricks), and lead a discussion on increasing diversity in the Earth, ocean, and atmospheric sciences.
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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.019 |
| Insufficient payload (model declined to judge) | 0.034 | 0.016 |
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".