Strategies for and Barriers to Collaboratively Developing Anti-racist Policies and Resources as Described by Geoscientists of Color Participating in the Unlearning Racism in Geoscience (URGE) Program
Bibliographic record
Abstract
The Unlearning Racism in Geosciences (URGE) program guides groups of geoscientists as they draft, implement, and assess anti-racist policies and resources for their workplace. Some participating Geoscientists of Color (GoC) shared concerns about microaggression, tokenism, and power struggles within their groups. These reports led us to collect and analyze data that describe the experiences of GoC in URGE. The data are from five discussion groups and two surveys. Our analyses revealed that participating GoC want to continue working with White colleagues on anti-racist work. GoC want White colleagues not to shy away from doing anti-racist work. Instead, GoC want White colleagues (1) to create and adhere to robust behavioral codes of conduct, (2) to focus discussions on anti-racism, (3) to act on anti-racism initiatives, (4) not to prompt GoC to educate them or reveal trauma, and (5) to refrain from microaggressions and tokenism. These desired outcomes were achieved in some groups with varying degrees of success. Correcting a history of mistrust relating to racism and anti-racism action is key to implementing and assessing effective anti-racist policies and resources. This requires leadership support, following through on anti-racism action, and deepening relationships between GoC and White colleagues. Future anti-racist programs should spend a substantial amount of time on and demonstrate the importance of training participants how to discuss racism effectively and how to create and adhere to robust behavioral codes of conduct. Future programs should also explore developing a robust program-wide code of conduct that includes a policy for reporting offenses.
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 | 0.025 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.012 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".