The Benefits of Informal Learning Garnered Through Participation in the Curriculum and Community Environmental Restoration Science (STEM + Computer Science) Project
Bibliographic record
Abstract
The Curriculum and Community Environmental Restoration Science (STEM + Computer Science) Project has several goals, with its primary focus on connecting the students of New York City with the enormous potential of restoring New York Harbor to its former self. Through the collaboration of numerous partners representing all of the facets of the city to the marginalized students living in hundreds of under-resourced communities, out-of-school experiences such as participation in the annual Science Symposium and the environmental fieldwork conducted along the shores of New York Harbor flourished. The CCERS STEM + C Project enables the merging of these entities for the good of its participants and the enormous benefit to the environment's restoration. This study consists of student surveys administered to and completed by 513 students attending schools throughout New York City's five boroughs. Of those who responded to the ethnicity section of the survey, 43.7% represent minority students. Data indicated a significant increase in STEM motivation by the CCERS STEM + C participating students, particularly those who identify as members of the under-represented minority group.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".