The Curriculum and Community Enterprise for Restoration Science: Engaging Marginalized Students in STEM Fields through Data Acquisition and Computational Thinking
Bibliographic record
Abstract
In an increasingly data driven world, the need for a qualified STEM workforce is essential. Increasing the diversity of this workforce increases the social and economic possibilities for the individual as well as national economic status and global prominence in innovation and technology. The Curriculum and Community Enterprise for Restoration Science has created a nexus between STEM education and possible college and career pathways in data science through ecological experiences in the classroom and in the field. Focusing on data collection and interpretation, New York City students from underserved communities are exposed to the local marine environment and efforts to restore aquatic species as well as the water quality in New York Harbor. The use of an innovative and motivating curriculum for both students and teachers bolstered confidence in acquired skills and content. By introducing students to these heretofore untapped STEM areas of interest, the BOP CCERS + STEM C Project has created opportunities in college and career options for underrepresented populations and the possibility of growing a more diverse STEM workforce.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".