Derrida, deconstruction and education : ethics of pedagogy and research
Bibliographic record
Abstract
Notes on Contributors. Preface. 1. Introduction: Derrida and the Philosophy of Education: Peter Trifonas (Ontario Institute for Studies in Education, University of Toronto). 2. Applied Derrida: (Mis)Reading the Work of Mourning in Educational Research: Patti Lather (Ohio State University). 3. The Teaching of Philosophy: Renewed Rights and Responsibilities: Denise Egea--Kuehne (Louisiana State University, Baton Rouge). 4. The Ethics of Science and/as Research: Deconstruction and the Orientations of a New Academic Responsibility: Peter Trifonas (Ontario Institute for Studies in Education, University of Toronto). 5. Archiving Derrida: Marla Morris (Georgia Southern University). Derrida, Pedagogy and the Calculation of the Subject: Michael A. Peters (University of Glasgow). 6. Signal Event Context: Trace Technologies of the Habit@online: Robert Luke (University of Toronto). 7. Dewey, Derrida and 'The Double Bind': Jim Garrison (Virginia Tech in Blacksburg, Virginia). Index.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".