Bibliographic record
Abstract
Throughout high school, University of New Hampshire sophomore Allie Collopy suffered through her required science courses and planned to do pretty much the same during her college career."I've never had a real aptitude for science," the UNH Spanish and linguistics major from Durham says matter-of-factly.But much to her surprise, after recently spending two weeks tromping through the Great Dismal Swamp and environs in North Carolina as part of UNH's Watershed Watch program, Collopy now plans to challenge herself with some rigorous science by minoring in an area of environmental conservation.Collopy, whose Watershed Watch project dealt with the effects of urbanization on water quality within the Pasquotank River watershed (which has its headwaters in the Great Dismal Swamp), hopes to gain some skills with her newfound aptitude for environmental science and eventually help people in third-world countries as a member of the Peace Corps."I always planned to go into the Peace Corps just to teach English, but I feel I have more options now," says Collopy who wants to launch a life for herself in South America where, she imagines, she might be able to help people grapple with water-related problems.Collopy's Watershed Watch experience is emblematic of the program created specifically to increase both the recruitment and retention of students into science, technology, engineering, and mathematics.Now in its second year, the National Science Foundation-funded Watershed Watch program is run out of the UNH Joan and James Leitzel Center and is a collaborative effort with Elizabeth City State University (ECSU) -a historically black university in North Carolina.Also participating are two-year colleges -the New Hampshire Community Technical College and North Carolina's College of the Albemarle.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".