Introducing Susceptibilities: Toward a Cultural Politics of Consent Under Erasure
Bibliographic record
Abstract
The broad aim of this introduction to a Special Issue on “Susceptibilities: Toward a Cultural Politics of Consent under Erasure” is to broach key questions and research directions that illuminate contemporary public debates about the conditions and limits of conscious intention (and consent as a byproduct thereof), which is typically treated as a “property” that can be “underdeveloped”, “given”, or “taken away”. In keeping with Jacques Derrida’s repudiation of the metaphysics of presence, the perspective animating this essay is that the psychoanalytic standpoint of the unconscious deconstructs the epistemological privilege of determinacy, consistency, and wholeness in treatments of intentional consciousness. Given Jean Laplanche’s attention to the residues of coherent ego fetishism in Sigmund Freud’s oeuvre, the former’s critique of self-sovereignty as evinced in his theorization of the “enigmatic signifier”, “primal repression”, and “afterwardsness” assumes a pivotal role in the analysis of how writers as represented here by Sarah Polley in Run Towards the Danger narrate the vicissitudes of their traumatic memories of sexual assault. Ultimately, then, the implications of this analysis will carry over to brief discussions of this Special Issue’s seven contributions by Melissa Wright, Karen McFadyen, J. Asher Godley, Madeleine Reddon, Gautam Basu Thakur, Robert Hughes, and Rebecca Saunders.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.074 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 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".