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Record W2925309034 · doi:10.5430/jct.v8n2p1

The Curriculum and Community Enterprise for Restoration Science Partnership Model

2019· article· en· W2925309034 on OpenAlexvenueno aff
Lauren Birney, D. McNamara, Brian Evans, Nancy Fúgate Woods, Jonathan Hill

Bibliographic record

VenueJournal of Curriculum and Teaching · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipStewardship (theology)Public relationsCurriculumEnvironmental stewardshipBusinessEngineering ethicsPolitical scienceKnowledge managementSociologyEngineeringEnvironmental resource managementPedagogyComputer science

Abstract

fetched live from OpenAlex

This paper identifies the complex interactions of a multi-member partnership and outlines the synergetic opportunitiesand challenges within the model. At the core of the partnership model is the restoration of the waterways surroundingNew York City through the reestablishment of the oyster into New York Harbor. The overarching goal was to connectmembers of the community to their environment to increase social awareness and responsibility. Stewardship of theharbor through involvement of education, business, and private sectors increased the citizen science involvement of thecommunity. The key to the success of this partnership model is the overlapping of roles and responsibilities as well asa strong “connector” serving to mediate the interactions among the stakeholders and enable the success of thepartnership. The partnerships were dynamic and evolving blurring lines and responsibilities. Serendipitous outcomesenhanced partnership relationships and in turn, the efficacy of the project.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0070.010
Scholarly communication0.0100.008
Open science0.0020.011
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0180.002

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 head0.029
GPT teacher head0.340
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2019
Admission routes1
Has abstractyes

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