Experiential and Real-World Learning for Teachers in the Billion Oyster Project and Curriculum and Community Enterprise (BOP-CCERS) for the Restoration of New York Harbor with New York City Public Schools Program
Bibliographic record
Abstract
The Billion Oyster Project and Curriculum and Community Enterprise for the Restoration of New York Harbor withNew York City Public Schools (BOP-CCERS) seeks to integrate harbor restoration activities with science teachers inorder to provide their students with experiential learning through environmental impact in New York City with thevision that public school students in New York City can benefit from environmental science and experiential learningwork through authentic research, data collection, and experimentation. The purpose is to engage science teachers withexperiential learning opportunities in the New York Harbor that helps them create engaging lessons for their ownstudents. It was found that teachers responded most positively to workshops that included hands-on activities,specifically the oyster restoration station trainings, classroom oyster tank setups and activities with scientists. Teachersreported that the BOP-CCERS program prepared them to support student learning of the program content and scientificresearch activities. Students who engage in real-world science are more likely to see the relevance of science and seethemselves working toward a career pathway in STEM.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".