BOOK REVIEW: Simon Battestini.<b>ECRITURE ET TEXTE: CONTRIBUTION AFRICAINE</b>. Qu�bec: Presses de l'Universit� Laval, 1997.
Bibliographic record
Abstract
Once in a while a book comes along that provokes and redefines the framework that has shaped and developed our thought, forcing us not only to rethink but reappraise and, eventually, reject fundamental yet unfounded notions and concepts that we have taken for granted. Ecriture et texte: contribution africaine is such a book. Arising out of the African experience, it is an original work that proposes a new definition of writing in relationship to definitions of semiotics, text, and culture. It presents the conclusions of an exhaustive forty-year research in Africa and elsewhere, demonstrating that Africa's relations with the West are based on an erroneous definition of what constitutes writing. It must be obvious that a book of such dimension will appear fragmented, and the diversity of the cases presented would seem to undermine any attempt at a synthesis. This is willfully intended, the author assures us, for as he explains in the avant-propos, "L'Afrique est multiple et diverse et le défaut majeur du discours africaniste, à nos yeux, est sa tendance à l'extrapolation et aux vastes généralisations" 'Africa is multiple and diverse and the major flaw of Africanist discourse, it seems to us, is its tendency towards extrapolation and vast generalizations' (19). Battestini thus invites the scientific world to critique its own discourse.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.084 | 0.077 |
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".