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Record W2584028638 · doi:10.21810/sfuer.v9i.309

Curriculum hidden: Contemplating more-than-human ethics

2016· article· en· W2584028638 on OpenAlexaffvenue
Lee Beavington

Bibliographic record

VenueSFU Educational Review · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsKwantlen Polytechnic UniversitySimon Fraser University
Fundersnot available
KeywordsAnthropocentrismMoralityCurriculumConversationEngineering ethicsSociologyHomo sapiensEnvironmental ethicsEpistemologyPsychologyPedagogyPhilosophyEngineeringCommunication

Abstract

fetched live from OpenAlex

Homo sapiens rationalize their species’ uniqueness to justify anthropocentrism and self-interest towards nonhumans. Research and pedagogical practice such as mandatory dissection and animal experimentation, alongside mechanistic and atomistic assumptions deeply embedded in science curricula, reinforce the idea that nonhuman animals lack moral status. We need to devise and implement holistic, place-based curricula in our schools, where ethics are part of the conversation before students use nonhuman animals in their learning and research. Synergy and deep ecology can move us from exclusively biotic morality toward a more inclusive abiotic morality.

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)0.010
metaresearch head score (Gemma)0.020
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 score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.008
Scholarly communication0.0030.006
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.363
Teacher spread0.308 · 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
Published2016
Admission routes2
Has abstractyes

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