keeping two distinct narrative follow both sets of ideas voices in your head at the same simultaneously and accurately. time. Ready? Well: you'll recall that Ready? Well: when I re-entered chapter before last I declared to my office the clock in the tower Mister Haecker that anyone who of the Municipal Building was wishes.. . just striking two... Have you got the knack? In Preface 3 you will have the opportunity to put your new-found reading skills to the test. Notes 1 Derrida, J. (1972) Positions, Chicago: University of Chicago Press, 1982 2 Derrida, J. (1972) Dissemination, London: Athlone, 1981, p.15 3 Ibid., p.7, emphasis in original 4 Ibid., p.75 Ibid., p.76 Spivak, G.C. Translator's preface. In J. Derrida (1967) Of Gramma-tology, Baltimore: The Johns Hopkins University Press, 1976, p.xii 7 Derrida (1972) Dissemination, op. cit., p.38 Johnson, B. Translator's preface. In Derrida, ibid., p.xxxii 9 Cixous, H. (1994) What is it o'clock? Or the door (we never enter). In H. Cixous Stigmata, London: Routledge, 1998, p.57 10 van Manen, M. (1990) Researching Lived Experience, Ontario: Althouse, 1997 11 Barthes, R. (1975) Roland Barthes, Basingstoke: Macmillan, 1995, p.56 12 Oxford Paperback Dictionary, Oxford: OUP 13 Derrida, J. (1967) Of Grammatology, op. cit., p.158 14 Ibid., p.158 15 Derrida (1972) Dissemination, op. cit. 16 Derrida, J. (1986) Glas, Lincoln: University of Nebraska Press, 1990 17 See translator's note in Derrida, J. Points..., Stanford: Stanford University Press, 1995, p.90
Bibliographic record
Abstract
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.033 | 0.016 |
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".