Bibliographic record
Abstract
Words and images interact with each other in art and everyday life and do so in many different ways. Building on recent trends in linguistic analysis and visual semiotics, a vibrant interdisciplinary field of inquiry called "word-and-image studies" has developed over the past few decades. Much of this new scholarship, however, has originated in the French-speaking world and thus has not been available in English - until now. Words and Images: A French Rendez-vous features six new essays translated from the French by Anthony Wall. These explorations spin an adventurous web through time - from the very beginnings of human language on prehistoric cave walls, to the textual background of early modern and Enlightenment art, to the coexistence of a poem and a coloured drawing on an exterior wall in contemporary Paris - and through interdisciplinary space, from archaeology and anthropology to art history, literary and communications theory, and philosophy of mind. The volume concludes with a bibliographical essay that provides an extensive summary of the most recent critical studies undertaken in France, Belgium, and Canada. With Contributions By: Bruno Nassim Aboudrar Pierre Civil Beatrice Fraenkel Stephane Lojkine Marie-Dominique Popelard Anthony Wall
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".