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Record W2782135457 · doi:10.5430/jct.v7n1p7

Advanced Placement Environmental Science and the Curriculum and Community Enterprise for Restoration Science (CCERS) Project in the New York City High School

2018· article· en· W2782135457 on OpenAlexvenueno aff
Lauren Birney, D. McNamara

Bibliographic record

VenueJournal of Curriculum and Teaching · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCourseworkAdvanced PlacementWork (physics)CurriculumEnvironmental educationEquity (law)Science educationSociologyEngineeringPolitical scienceEngineering ethicsPedagogy

Abstract

fetched live from OpenAlex

This paper explores the issue of social justice through the lens of equitable access to Advanced Placement courses inthe City of New York High Schools, with focus on Advanced Placement Environmental Science. A criticalcomponent of the Advanced Placement Environmental Science course is the incorporation of environmentalfieldwork. The National Research Council (2014) suggest that field stations are important for STEM education andprovide opportunities to engage students in the natural environment and get them excited about science. Through theCurriculum and Community Enterprise for Restoration Science, an NSF funded opportunity, students in theAdvanced Placement Environmental Science course are integrating their field station work in Oyster Restoration inthe New York City Harbor. These interactions with the environment offer unique experiences which engagemarginalized students in both rigorous coursework and affords equity in science learning. In turn, it affords allstudents the opportunity for upward mobility and increased career opportunities in the area of 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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.290
Teacher spread0.260 · 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.

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

Citations2
Published2018
Admission routes1
Has abstractyes

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