Canadian Journal of Sociology Online November-December 2006
Bibliographic record
Abstract
answers this question negatively: “Don’t read him writing on the death of others … Don’t talk too much about yourself … Don’t make too much of his own ‘last words ’ in Le Monde on 19 August, 2004. ” Gaston also found a positive way to proceed – to read Glas, the only book he says he could read – a book that tells “you how not to monu-memorialize, to idealize and interiorize the ‘father ’ as an act of mourning ” (2). Gaston successfully follows Derrida’s warning against the dangers of mourning. This is especially difficult as he is writing at a time when the sense of loss is profound, the fifty-two days following Derrida’s funeral, when the dangers of reappropriation, narcissistic pathos, and cannibalistic consumption of the other are so present (Derrida, 2001: 159, 168, 225.) Gaston’s focus is the gap because with representation a gap immediately appears between a thing and its double. We find Gaston unwilling and unable to mourn the death of Derrida. Rather than visiting Derrida’s grave when in Paris, he goes to the book shops PuF and Vrin at Place de la Sorbonne and La Hune on Saint Germain where he buys more of Derrida’s books – to keep reading – to keep writing – in the gap between death and a time of mourning which can never arrive, but arrived the first time we read Derrida, and knew one day the great vessel would lay emptied of its prose. We know this with all writers who move us, who touch us in some deeper way, and reading
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.340 | 0.079 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".