Enhancing equity, diversity, and inclusion in physics: perspectives from North American underground laboratories
Bibliographic record
Abstract
Equity, Diversity, and Inclusion (EDI) are important to drive innovation in many different fields, including particle physics. Underground labs are working on many different fronts to improve EDI in their host countries and within particle physics collaborations. Laboratories can institute policies to protect their staff and make improvements to their facilities to increase accessibility. Laboratories can encourage the scientific collaborations they host to have policies and plans for increasing EDI. SNOLAB and the Sanford Underground Research Facility (SURF) are each supporting their employees and user-bases in different ways. Some examples are targeted outreach, consultation with experimental collaborations on their own policies, EDI training, and Indigenous cultural recognition. These efforts are intended to enhance the equity and inclusion of their communities.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.044 | 0.020 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".