The Billion Oyster Project and Curriculum and Community Enterprise for Restoration Science Curriculum Impact on Teacher Engagement
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 of multiple institutions and organizations led by Pace University. The NSF project, Innovative Technology Experiences for Students and Teachers (ITEST), had generated a large amount of data through engagement with teachers and students throughout New York City public schools. This article presents the second part to a large data collection study with focus on Underrepresented Minority (URM) student interest in STEM and engagement with teachers to support them in teaching science through experiential learning and lessons that connect science to the real world, particularly through science in the New York Harbor. The first component of the study focused on URM student interest in STEM. This second component of the study focuses on teacher engagement in the program, and what the researchers had learned in the process. Overall, teachers reported very favorable options on the impact of the BOP-CCERS activities as ways to generate student interest in STEM majors and careers. Teacher participants were generally positive about the amount of support and resources they received as members of the project, as well as the oyster-related knowledge and practices they learned to use with their own students in oyster field research. Data from the study provided evidence that the teacher activities were successful and met the project’s goals to provide support and resources for teachers to engage students in oyster restoration research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".