All in a Year’s Work: Achievements toward Entomology for All
Bibliographic record
Abstract
ENGAGING AND SUPPORTING STUDENTS FROM UNDERREPRESENTED COMMUNITIES DURING THEIR CAREER DEVELOPMENT IS ESSENTIAL FOR INCREASING DIVERSITY WITHIN ESA. As we advance Diversity & Inclusion (D&I) efforts within ESA, we wanted to highlight some of the year’s successes and future plans to continue actively moving forward in promoting diversity, inclusion, recruitment, and retention over all. This is key to ensuring that ESA is a scientific society that cultivates excellence, tolerance, and mutual respect. The D&I committee, chaired by Hongmei Li-Byarlay, an associate professor of entomology at Central State University of Wilberforce, Ohio, was reformed to a six subcommittee structure in 2022: The committee and subcommittees meet monthly to propose resources, programs, and services that will maximize D&I support amongst ESA members. As part of this effort, the committee co-organized several sessions for this year’s ESA Joint Annual Meeting that took place in Vancouver, Canada (13-16 Nov. ): To celebrate the first-year anniversary of the Entomology for All column (Manrique et al. 2021), we wanted to highlight three programs and initiatives that reflect some of the work that the D&I committee has focused on over the last year and provide some future perspectives for more initiatives, new resources, and impactful programs aimed at promoting inclusion and diversity within the society.
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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 | 0.012 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".