CCERS STEM + C – Emphasis on the Professional Learning of the Classroom Teachers – Expansion of the Pillar
Bibliographic record
Abstract
This qualitative study chronicles one of the fundamental pillars of the Curriculum and Community Enterprise for Restoration Science (CCERS). The professional development is focused on curricula that are grounded in the community-based environmental restoration of the waterways of New York Harbor. Centered on the restoration of the native oyster population, hundreds of New York City public school teachers take part in this experience with the intent of increasing their own place-based pedagogical content knowledge and skills. Most of the participants teach in school with populations that are underrepresented in post-secondary STEM majors and STEM related careers. Professional learning activities for teachers and community scientists were offered throughout the 2021 calendar year. Professional Learning Activity Surveys were administered and teachers responded to questions about how they participated in CCERS events, the ways in which CCERS participation has impacted their teaching practice, whether they use CCERS activities for student research, and ways CCERS participation impacts student STEM career interest. An intended outcome is to instill a STEM identity in students identifying as URM and to bring STEM career awareness to these students. More than 72% of the teachers in the professional development sessions agreed that the professional learning activities were effective in providing new STEM content knowledge and best practices for teaching. The majority also reported that the sessions enabled them to increase their students’ engagement with STEM and interest in STEM careers.
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 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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".