The Billion Oyster Project and Curriculum and Community Enterprise for Restoration Science Curriculum: The Digital Platform and Student Symposium Presentations
Bibliographic record
Abstract
The Billion Oyster Project and Curriculum and Community Enterprise for the Restoration of New York Harbor (BOP-CCERS) program is a National Science Foundation (NSF) supported initiative and collaboration of multiple institutions and organizations led by Pace University and is in collaboration with New York City Public Schools. This large-scale project, Innovative Technology Experiences for Students and Teachers (ITEST), generated a large amount of data through programming that engaged both teachers and students. This article presents the third part to the study with focus on the Digital Platform and results from the student Symposium presentations. Part 1 focused on Underrepresented Minority (URM) student interest in STEM as motivated by the original project. Part 2 focused on URM student engagement with teachers to support students in teaching science through experiential learning and lessons that connect science to the real world, particularly through science in the New York Harbor. Moreover, the second part of the study focused on teacher engagement in the program, and what the researchers had learned in the process. The third aspect of the study, and primary focus on this paper, had found additional positive results cited by the teachers in the study both using the Digital Platform and after the project outcomes from the student Symposium presentations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".