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Record W4402999649 · doi:10.57125/fp.2024.12.30.03

Education and Artificial Intelligence at the Scene of Writing: A Derridean Consideration

2024· article· en· W4402999649 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFuturity Philosophy. · 2024
Typearticle
Languageen
FieldComputer Science
TopicArtificial Intelligence in Education
Canadian institutionsSimon Fraser University
FundersSimon Fraser University
KeywordsArtificial intelligenceCognitive scienceComputer sciencePsychology

Abstract

fetched live from OpenAlex

Skepticism of the written word has been prevalent in philosophical discourse at least since the works of Plato. This article employs philosophical method. It situates the ongoing educational concern with AI Chatbots in terms of this skepticism toward writing. Specifically, this longstanding skepticism posits that the written word is an alienated form of the spoken word. This article demonstrates how two prevalent traditions of education—traditional and progressive—take up this same skepticism. The article calls upon the work of Jacques Derrida, whose deconstructive theories on Plato and the written word problematize this line of writerly skepticism. Derrida’s work on Rousseau’s Emile informs a more general approach to pedagogy which entails what Derrida calls “the logic of supplementarity .” This “logic” involves the paradoxical debt that writing owes to speech. Thus, one can discern a distinct sense in which education—in general—is implicated in a tension that exists between the written word to the spoken. Ultimately, this articles suggests that the ongoing concern with AI Chatbots—linked to an ancient skepticism toward writing—is none other than a concern with the very practice of education per se.

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.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.052
GPT teacher head0.327
Teacher spread0.274 · 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