WWU AS Environmental Justice Program Intern
Bibliographic record
Abstract
Because I was the only person in the ESP, I was the clear leader of the ESP but did not have the ESP director title. Despite this, I took on various roles of the director, a few of which were seats on committees. As a voting member of the Sustainability, Equity, and Justice Fund (SEJF) committee, I participated in biweekly meetings where we discussed incoming grant proposals. We saw proposals for many kinds of projects including travel to conferences for men of color in academia, film screenings on mental health in indigenous communities, work parties in environmental restoration for LEAD, and so many more. In the first few months, we approved nearly every grant we saw. SEJF had a surplus of money due to a deficit of proposals during the COVID years, so we were able to fund a wider range of projects. In addition to voting on the proposals, it was also important for me to promote SEJF to clubs and students, so that they knew there was a funding body which wanted to support their ideas for community and personal academic and professional development. Along with other SEJF members, I helped get the word out about the grant to Western in a post-COVID era where so many were unaware of what Western had to offer to students. It ended up being a successful campaign – by spring quarter we had to be far more selective about which projects to fund because the volume of proposals increased rapidly.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.348 | 0.072 |
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".