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Record W1529018351 · doi:10.7202/1072426ar

Andragogical Epistemology and the Lacanian Discourse of the Hysteric: Learning Through Trauma, Trauma Through Learning

2020· article· en· W1529018351 on OpenAlexaffvenue
Michael D. Berry

Bibliographic record

VenuePaideusis · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAndragogyPrimum non nocereHarmReflexivityCritical thinkingEpistemologySociologyCriticismPsychologyPedagogySocial psychologyPhilosophySocial scienceAdult educationLiteratureArt

Abstract

fetched live from OpenAlex

Harm, this paper proposes, is a viable teaching objective. Presenting an andragogy of post secondary liberal arts education, this paper explores the relationship between critical thinking and subjective harm, arguing that subjective harm is an inevitable outcome of critical thinking practice. The author situates this teaching methodology within the discourse theory of psychoanalyst Jacques Lacan, positioning this critical thinking andragogy specifically within the Discourse of the Hysteric, which interrogates the institutionalized epistemology present in universities. Defining critical thinking as a subjective cognitive technique adjoined to reflexivity and reflective practice, this paper examines and refutes the university principle of primum non nocere (do no harm), arguing that it represents a logical incoherence in university principles. Semantically and conceptually examining the terminologies involved, the author contends that, to the extent that we accept the definition of “harm” found in the Discourse of the University, any teaching practice that valorizes critical thinking inevitably will, and should, be harmful.

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)0.007
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 score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0090.086
Scholarly communication0.0090.007
Open science0.0010.009
Research integrity0.0030.005
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 head0.118
GPT teacher head0.400
Teacher spread0.282 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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