<i>Le (Beau) Risque d’écrire: entretiens littéraires</i> . Par <scp>Karin Schwerdtner</scp> <i>Le (Beau) Risque d’écrire: entretiens littéraires</i> . Par SchwerdtnerKarin. Québec: Nota bene, 2017. 236 pp.
Bibliographic record
Abstract
What is at stake for women writers whose work pushes against convention and challenges existing practice? How might the self be endangered by writing? What kinds of risk are produced by the contemporary publishing industry and its various pressures? Is discussing one’s own work for promotional purposes a risky business in itself? In this collection of twelve interviews with established women writers, conducted between 2011 and 2015, Karin Schwerdtner pursues her sense that female authors are increasingly preoccupied with risk, and addresses the issue of writing and/as risk at numerous levels. The interviews are in several cases very substantial in scope and contain a good number of valuable insights into the specific idiosyncrasies of individual practice and the idea of risk more generally. Risk is seen to be associated with writing as a woman, but also to extend beyond gender-determined parameters. The authors interviewed, Annie Ernaux, Chantal Chawaf, Marie Nimier, Linda Lê, Camille Laurens, Cécile Oumhani, Leïla Sebbar, Laurence Nobécourt, Hélène Lenoir, Sylvie Germain, Agnès Desarthe, and Maryline Desbiolles, explore risk as it applies to their own writing practice and analyse their evolving relationship to it. A good deal of fascinating matter emerges. While readers of Ernaux may already understand that writing is of value for the author only if it entails a fresh sense of danger, the interview’s concentration on this issue is revealing and we note with interest the author’s reference to her private journal as a particularly dangerous text that will be published only after her death. Several other writers, notably Nimier, Lê, and Laurens, follow Ernaux in discussing the dangers inherent in life writing, especially in autofiction. Chawaf explores her writing as an activity made up of different layers of danger, while Sebbar speaks of risk avoidance in her writing, and Desbiolles is concerned with the risk of succumbing to an urge to write no more. While this collection does not propose to create links between the interviews, it nonetheless permits the reader to begin tentatively to map risk. Its value lies not only in the rich material provided in its (admittedly somewhat random) collection of interviews, but in the as-yet-unexplored research spaces that it points to: how might risk as a key component of contemporary women’s writing in French be theorized? How does it play out in different genres? How is it conjured by other writers associated with risk, such as Christine Angot, Catherine Millet, Hélène Cixous, Marie Darrieussecq, or Marie NDiaye? And what kinds of risk are specifically experienced and evoked by francophone women writers? In short, this book is both a useful research tool and an invigorating read.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 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".