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Preparing Teachers for Work in a Time of Environmental Crises: A Grass-roots Response

2023· article· en· W4384455799 on OpenAlexaffabout
P. M. Elliott

Bibliographic record

VenueInternational Journal for Cross-Disciplinary Subjects in Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsTrent University
Fundersnot available
KeywordsWork (physics)Environmental scienceEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Our planet faces numerous environmental threats of anthropogenic cause, including climate chaos, plastic pollution and deforestation.All of these impact biodiversityour life support mechanism.With regard to this, Education can be said to have failed humanity and all living things by largely ignoring these issues and by being complicit in a way of living on the planet that is not sustainable.There is a growing concern that education systems are making environmental problems worse by prioritizing the preparation of young people for an unsustainable economic paradigm, and framing success in terms of economic wealth and consumption.Education should now have a crucial role to play in helping to address, minimize and mitigate the impact environmental threats while helping to build a sustainable future.Initial teacher education programmes can be seen as a driver of change in education systems so it can be argued that they should be preparing new teachers for this existentially important work.To date, however, teacher education programmes in Canada have generally done a poor job of preparing student teachers for environmental and sustainability education.This paper reviews the collaborative strategies developed and employed by a group of teacher educators in Canada who have sought to address the shortfall in environmental and sustainability education in pre-service teacher education.Their grass-roots work has drawn attention to the issue and advocated for change, both within institutions and more widely.It has also created new research opportunities and by sharing success stories significant progress in praxis has been made in recent years.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.386
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.078
GPT teacher head0.460
Teacher spread0.382 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal for Cross-Disciplinary Subjects in EducationSame topicTeacher Education and Leadership StudiesFrench-language works237,207