Bibliographic record
Abstract
This paper explores how educators might intervene in canonized texts of the human subject on which a particular and exclusive kind of humanism rests. In imagining possible interventions educators might make, I turn to and trace Jacques Derrida's on‐going deconstruction of the philosophical texts of subjectivity. In his body of work, Derrida destabilizes fixed notions of the human subject and the institutions it founds (like philosophy and education). From Derrida's points of destabilization and through a differing but similar deconstructive stance, I also consider Gayatri Spivak's suggestive question ‘Who is not the subject of humanism?’ to provide another possible trajectory for intervention that educators might take. Departing from knowledge‐based conceptions of human subjectivity, Spivak urges educators to respond to their students in meaningful encounter with the ‘Other’ while Derrida suggests human beings might begin the difficult and complex task of re‐envisioning an altered humanism, a humanism founded on the call of the Other in institutional sites like education. By an engaged rereading of the texts of human subjectivity upon which human beings are written and by turning to respond to the face of the human beings in and outside their classrooms as a means of encountering the Other's humanity, I suggest that educators be the catalyst for changing what it means to be human and education the means by which we approach a humanism yet to be.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.096 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".