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Record W7071382237

Six Years of Adult Education about the "Wonders of the Salish Sea"

2022· article· en· W7071382237 on OpenAlexaboutno aff

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsnot available
Fundersnot available
KeywordsAttendanceAdult educationStakeholderCommunity educationEnvironmental education
DOInot available

Abstract

fetched live from OpenAlex

The "Wonders of the Salish Sea" (WSS) is an environmental education program designed to connect residents to the Salish Sea ecosystem through community-based, awe-inspiring education. The program aims to create a community of citizens who love, care for, and want to protect the Salish Sea; and to provide an opportunity for local scientists, naturalists, environmentalists, and enthusiasts to share their passion and expertise with the general public. Started in 2016 in Vancouver BC, WSS fills a gap in education by meeting the needs of beginning youth and adult learners who want to gain an in-depth understanding of the ecosystem in which they live. The program moved to a virtual format in 2021, making it accessible to all residents of the Salish Sea and beyond. WSS is held over a 4 - 5 week period annually in the spring. Attendance has grown every year with many returnees. The response from stakeholders has been overwhelmingly positive. For example, participants have said: "It has greatly increased my desire to preserve the Salish Sea and it's inhabitants," "I think the ripple effects of this kind of education cannot be tabulated," and "I've always been careful about what I advocate for but since I took this course I've written 3 letters." This poster will highlight the program model, participant data, outcomes, stakeholder feedback, spin-off initiatives, and 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.

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)0.000
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 score0.535
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.246
Teacher spread0.231 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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