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Record W2984292196 · doi:10.5430/jct.v8n4p30

Experiential and Real-World Learning for Teachers in the Billion Oyster Project and Curriculum and Community Enterprise (BOP-CCERS) for the Restoration of New York Harbor with New York City Public Schools Program

2019· article· en· W2984292196 on OpenAlexvenueno aff
Lauren Birney, Joyce Kong, Brian R. Evans, Samantha Ceritelli, Macey Danker

Bibliographic record

VenueJournal of Curriculum and Teaching · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsExperiential learningCurriculumExperiential educationOysterEnvironmental educationEngineeringSociologyPublic relationsPedagogyPolitical sciencePsychologyEcology

Abstract

fetched live from OpenAlex

The Billion Oyster Project and Curriculum and Community Enterprise for the Restoration of New York Harbor withNew York City Public Schools (BOP-CCERS) seeks to integrate harbor restoration activities with science teachers inorder to provide their students with experiential learning through environmental impact in New York City with thevision that public school students in New York City can benefit from environmental science and experiential learningwork through authentic research, data collection, and experimentation. The purpose is to engage science teachers withexperiential learning opportunities in the New York Harbor that helps them create engaging lessons for their ownstudents. It was found that teachers responded most positively to workshops that included hands-on activities,specifically the oyster restoration station trainings, classroom oyster tank setups and activities with scientists. Teachersreported that the BOP-CCERS program prepared them to support student learning of the program content and scientificresearch activities. Students who engage in real-world science are more likely to see the relevance of science and seethemselves working toward a career pathway in STEM.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.316
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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