The Billion Oyster Project and Curriculum and Community Enterprise for Restoration Science Curriculum: Summary of STEM+C and ITEST Program Impacts on NYC Teachers and Students
Bibliographic record
Abstract
For over a decade, the Billion Oyster Project and Curriculum and Community Enterprise for the Restoration of New York Harbor (BOP-CCERS) Educational Program has supported New York City teachers and students through experiential learning in science education. The program has been supported by over $10 million dollars of award support from the National Science Foundation (NSF) and has involved multiple community stakeholder collaborations led by Pace University. Within the University, the initiative spanned three different schools and colleges with collaboration through many academic departments. Two grants in the project have been the Science, Technology, Engineering, and Mathematics plus Computing (STEM+C) and Innovative Technology Experiences for Students and Teachers (ITEST) program. The purpose of this article is to summarize the major impacts this integrated program has had on New York City teachers and students designed to provide teachers with experiential education support and engage students to improve their STEM education and encourage further STEM studies and career pathways. Results from over a decade of programming, vast amounts of data collection and analysis, and multiple research studies have indicated considerable achievements for the program involving both support for teachers and engagement for students. The long-term outcome is improved student STEM achievement and recruitment and retention of diverse students in STEM college programs and careers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".