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Record W4283317107 · doi:10.14516/ete.515

From Scarcity to Abundance: Illich’s Educational Critique and Indigenous Learning

2022· article· en· W4283317107 on OpenAlexaff
Chris Beeman

Bibliographic record

VenueEspacio Tiempo y Educación · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Methods and Practices
Canadian institutionsBrandon UniversityQueen's University
Fundersnot available
KeywordsIndigenousConceptualizationContext (archaeology)ScarcitySociologyEnvironmental ethicsIndigenous educationDocumentationHistoryPolitical scienceEcologyArchaeologyPhilosophy

Abstract

fetched live from OpenAlex

This paper takes as a beginning point Ivan Illich’s radical work on education and schooling, which began with his posting to Puerto Rico as vice-rector of the Catholic University at Ponce, in Puerto Rico, in 1956. This work continued with the Centre for Intercultural Documentation (CIDOC), in Cuernavaca, Mexico, through the publication of Deschooling Society (1971) and beyond. Three distinct phases in Illich’s conceptualization of schooling and education are traced. For the purpose of this paper, I will term them de-mythologizing, radical scrutinizing, and re-tooling). In the third phase, Illich and his colleague Edward Reimer posited that what is actually needed in reconsidering education is to improve human interaction with the tool of education. This insight formed part of Illich’s 1973 book, Tools for Conviviality, in which he explored what such a project might look like. In this paper, this idea is pushed further. I offer some stories of Teme Augama Anishinaabe Elders from Turtle Island (North America) with whom I have worked for several years, along with my reflections, to suggest an altogether different view of learning and education, one which takes place in a context of abundance.

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)0.006
metaresearch head score (Gemma)0.011
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 score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0160.085
Scholarly communication0.0080.011
Open science0.0020.009
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.413
Teacher spread0.389 · 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
Published2022
Admission routes1
Has abstractyes

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