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Record W2936750417

Education and the Land Ethic

2019· article· en· W2936750417 on OpenAlexaff
Lee Beavington, David Chang

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsSimon Fraser UniversityKwantlen Polytechnic University
Fundersnot available
KeywordsContemplationExperiential learningEnvironmental ethicsSociologyPedagogyEcologyAestheticsEpistemologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Working from Leopold’s land ethic as a theoretical frame for education, this session presents two modes of relationship to land: active and contemplative. On the active side, the presenters demonstrate the use of the GPS ecocache via an experiential, place-based activity, where learners navigate sites of ecological significance and answer a question or riddle related to this site. On the contemplative side, we propose practices that connect the senses to the land, including techniques of priming attention so as to open learners to the presence of the more-than-human. We hope to enact the values of Leopold’s land ethic through the use of a ubiquitously available device, while at the same time re-interpret Leopold’s writing through contemplative practices that align with land ethics and place-based education. In attending the session, participants engage in relational pedagogy via ecology, geography, environmental ethics, philosophy of science, and philosophy of education, through experiential activities that forefront the UBC campus and its surrounding lands on traditional Musqueam territory.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.979
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.054
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.294
Teacher spread0.272 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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