On The Legacies of Derrida and Deconstruction Today: An Interview with Jean-Michel Rabaté
Bibliographic record
Abstract
In this interview, which took place in Center City in Philadelphia in September 2020, I ask Jean-Michel Rabaté to reflect on his personal and writerly relationship with Jacques Derrida, and to assess the legacies of Derrida and deconstruction across the globe today. In the last five years, Rabaté has published three books (one monograph and two edited volumes) on Derrida: Les Guerres de Derrida (Presses de l'Université de Montréal, 2016), After Derrida (Cambridge University Press, 2018), and Understanding Derrida, Understanding Modernism (Bloomsbury, 2019). In response to this flurry of publications, I ask Rabaté what has prompted his recent and vigorous turn to Derrida and what his intentions were with these books. As we review their organising principles, the central thematic of this discussion is thus concerned with Derrida's relevance to the Humanities and Social Sciences as we enter the third decade of the twenty-first century.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.036 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".