The Billion Oyster Project and Curriculum and Community Enterprise for Restoration Science Curriculum: STEM+C Summer Institute Experiential Learning
Bibliographic record
Abstract
The Billion Oyster Project and Curriculum and Community Enterprise for the Restoration of New York Harbor with New York City Public Schools (BOP-CCERS) program is a National Science Foundation (NSF) supported initiative and collaboration led by Pace University. One of Pace University’s NSF projects is STEM+C (Science, Technology, Engineering, and Mathematics plus Computing) designed to work with teachers and students in New York City public schools. This article presents results of a study conducted on the STEM Summer Institute at Pace University in Summer 2022. The purpose was to engage both teachers and students in harbor restoration and experiential learning in New York City including learning about vital ecology projects related to New York’s harbor such as oyster restoration, which is critical to cleaning pollutants in the New York Harbor. Findings revealed that students indicated improved oyster knowledge and restoration skills, scientific skills, collecting and analyzing data, and knowledge about STEM careers. Participating teachers indicated a positive impact on their knowledge of content and harbor restoration, pedagogical knowledge used to engage students in hands-on scientific learning, and methods of engaging and motivating their own students. Moreover, teachers indicated a positive outcome for exposing their students to STEM career options.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".