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Record W2886784186 · doi:10.5430/jct.v7n2p20

Teachers Mentoring Teachers in the Billion Oyster Project and Curriculum and Community Enterprise for the Restoration of New York Harbor with New York City Public Schools (BOP-CCERS) Fellowship

2018· article· en· W2886784186 on OpenAlexvenueno aff
Lauren Birney, Joyce Kong, Brian R. Evans, Ashley M Persuad, Macey Danker

Bibliographic record

VenueJournal of Curriculum and Teaching · 2018
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsCurriculumHospitalityOysterLibrary sciencePolitical scienceManagementSociologyMedical educationEngineeringPedagogyMedicineEcology

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)(NSF DRL 1440869/PI Lauren Birney) program is a National ScienceFoundation (NSF) supported initiative through collaboration by multiple institutions and organizations led by PaceUniversity. Partners on this initiatitve include Columbia Lamont Doherty, the New York Aquairum, the New YorkHarbor Foundation, the New York Academy of Sciences, the River Project, Good Shepher Services, SmartstartEvaluation and Research, the University Maryland Center for Environmental Science and Fearless Solutions. Inthis study, teachers from one cohort were paired with teachers from a succeeding cohort in order to facilitate amentoring process between the two cohorts. This allows for teacher ambassardors to have a support structurethroughout the program, seek integral feedback, modify teaching techniques, integrate project research and establishlong term partnerships within the project team.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
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 integrity0.0000.002
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 head0.081
GPT teacher head0.338
Teacher spread0.257 · 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
Published2018
Admission routes1
Has abstractyes

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