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Record W2945129864 · doi:10.23882/mj1905

The Ecology of Teaching & Learning (Science)

2019· article· en· W2945129864 on OpenAlex
Angus McMurtry, Giuliano Reis

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuerevistamultidisciplinar com · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPerspective (graphical)SociologyScience educationSustainable developmentEcologyMathematics educationPsychologyEngineering ethicsPedagogyComputer scienceBiologyArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

The present position paper articulates insights of complexity science, a progressive approach to understanding living systems that is compatible with critical perspectives on teaching and learning. Drawing from examples of an outdoor activity in a teacher education science methods course, we argue that complexity science offers an ecological perspective on education itself. That is, learning and teaching are understood as nurturing students to adaptively reorganize their belief systems to adjust to larger biological, social and cultural practices that are themselves constantly evolving. The infusion of complexity theory into education – and the associated development of a wider appreciation for the intricate nature of teaching and learning processes – not only makes it more likely for teachers and students to be able to interact effectively with(in) the world in multileveled and relational ways, but it also empowers (provokes) them to act upon current global socio-ecological problems in more just and sustainable ways.

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.

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)0.016
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.086
GPT teacher head0.418
Teacher spread0.332 · 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