Bibliographic record
Abstract
A bstract Ethics penetrates every aspect of Western education. Many of its dominant narratives — education as salvation, as progress, as panacea, and as liberation, for example — are infused with the ethical. Educators are compelled by ethical callings; in fact, education as the call of the ethical informs the singular and collective identities of educators. In this essay, Aparna Mishra Tarc troubles the role of the ethics in Western education using Gayatri Chakravorty Spivak’s deconstruction of the ethical in philosophy. Spivak’s deconstruction reveals how an ethics based in one’s idea of what the Other is and should be violates the uniqueness of Others. Spivak challenges educators to examine vigilantly the ethico‐political assumptions and discourses underlying ethical acts in the classroom. Drawing on the writings of Emmanuel Levinas, mediated by the thinking of Jacques Derrida, Spivak re‐imagines ethics apart from — and as a part of — its metaphysical heritage. Tarc discusses aspects of Spivak’s vision for an education borne out of ethical singularity as hearing and responding to the Other’s call. Finally, she explores its implications for how one might begin to respond justly to the conditions of others within and alongside of one’s own intellectual and pedagogical engagements.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.075 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".