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An Ethics of Repair? Towards Reparative Principles in the History of Education

2024· article· en· W4407565908 on OpenAlexvenueno aff
Mati Keynes

Bibliographic record

VenueEncounters in Theory and History of Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPeace and Human Rights Education
Canadian institutionsnot available
Fundersnot available
KeywordsRedressExceptionalismSociologyEconomic JusticeAnthropocentrismEnvironmental ethicsDeconstruction (building)ExplicationLawEngineering ethicsPolitical scienceEpistemologyEngineering

Abstract

fetched live from OpenAlex

Histories of education are closely entwined with agendas of reparative justice, redress and reconciliation. Educational questions, past and present, have been central to recent debates about redress, and historical thinking has a vital role to play in making sense of the afterlives of violence that are history’s present. This includes exposing the role of education in justifying human exceptionalism and legitimating violence as well as radically re-historicising educational pasts from entangled, decolonial, and post-anthropocentric perspectives, work that is already underway. This conceptual paper takes up these intersecting imperatives for repair and the revaluation of historical research in education. It asks: what might reparative histories of education look like? What might constitute an ethics of repair for the history of education? To address these questions, three principles concerned with the repair of past injustices are canvassed: complex implication, care and concern, and legibility. The goal is not to normatively prescribe or evaluate principles, but to explore how these are already informing some kinds of historical work, and to provoke dialogue about how we might develop reparative dimensions to our work in a world that desperately needs repair.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.053
GPT teacher head0.382
Teacher spread0.329 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

Explore more

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