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Record W3215417619 · doi:10.1080/13642529.2021.1992159

A ‘wicked problem’: rethinking history education in the Anthropocene

2021· article· en· W3215417619 on OpenAlexaffabout
Heather E. McGregor, Jackson Pind, Sara Karn

Bibliographic record

VenueRethinking History · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsQueen's University
Fundersnot available
KeywordsAnthropoceneEnvironmental ethicsScholarshipIndigenousMeaning (existential)Deep timeSituatedHumanitySociologyEcological crisisHistoryEpistemologyAestheticsEcologyPolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

In seeking to attune history education to a relational, ecological, and ethical future orientation, we turned to scholarship in other fields that teach similar or proximate outcomes: Indigenous studies, environmental history, and climate change education. We suggest that the challenge in history is not just teaching about climate variation over time and its consequences, but also recognizing that the Anthropocene is a multidimensional phenomenon requiring adaptation in ways of being and understanding ourselves. We draw on the literature in each of the above-mentioned fields to leverage theory, content, and pedagogical cues to begin envisioning how history teachers and learners can seek meaning, when the terms within which we have made meaning in the past may slip away. In this article, we offer a prospective agenda for provoking history education to make significant change, particularly in Canada where we are situated. Our suggestions for history teaching and learning practice may be deployed in many different contexts to help educators confront the climate crisis. As historians and educators, we must provide these opportunities to learn about the past because as Davis and Todd state, 'the story we tell ourselves about environmental crises, the story of humanity's place on the earth and its presence within geological time determines how we understand how we got here, where we might like to be headed, and what we need to do'.

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 categoriesInsufficient 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: none
Teacher disagreement score0.875
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.134
GPT teacher head0.367
Teacher spread0.233 · 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.

Study designNot applicable
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

Citations21
Published2021
Admission routes2
Has abstractyes

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