Tackling Problem-Solving Through the Curriculum and Community Enterprise for Environmental Restoration Project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of this study is to showcase the use of incorporating problem-solving in a multifaceted, long-term investigation. New York Harbor and its estuaries are undergoing a major revitalization due to the efforts of the Curriculum and Community Enterprise for Environmental Restoration Project. Comprised of a network of local educational and business partnerships, students who have been historically underrepresented in the S.T.E.M. field are given the opportunity to delve into environmental restoration in their local communities and strategize, analyze and evaluate environmental challenges to achieve success in problem-solving. The restoration of a sustainable environment is reliant upon innovative responses to the challenges posed. Problem-solving allows the students to use advanced thinking ability and it can also be the driving force of change. The project has resulted in a deeper understanding of local environmental restoration efforts and a stronger commitment to actionable plans for the future.
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.
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 | 0.004 | 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 | 0.003 | 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 it