Bibliographic record
Abstract
Deconstruction Mary Chapman, Professor To paraphrase Virginia Woolf, the literary field changed (for me) “on or about” September 1982. I was finishing my ba and taking a literary criticism survey with the recently hired Tilottama Rajan. Trained in the comfortable paradoxes of New Criticism’s “well wrought urn,” I felt the ground shift under me when I learned about Deconstruction. The idea that representation was an equivocal process, that différance was endemic to language, was terrifying. It was hard not to consider Derrida’s “unworking of language” nihilist. And yet years later, it is exciting to see the productive impact on literary study that deconstruction has had, particularly on inherited concepts like gender, race, class, nation, and sexuality. The methodological tools Deconstruction gave us have renewed entire subfields of literary study because, as Dana Luciano and Ivy G. Wilson suggest in Unsettled States: Nineteenth-Century American Literary Studies, even the temporal, national, spatial, and disciplinary terms by which we understand our work have come under interrogation. [End Page 7] Mary Chapman, Professor English, University of British Columbia Copyright © 2015 Association of Canadian College and University Teachers
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".