Bibliographic record
Abstract
Lecture 7 examines the application of rhetorical principles to images to see how they are imagined to be persuasive and the ways in which that power of persuasion has been theorized. Whether through discourse or imagery, the notion of persuasion as understood in relation to images is accounted for in a discussion ranging from Plato to William Wordsworth, from Roland Barthes to Paul Messaris, from metaphysics to idolatry. Résumé Le septième cours examine l’application de principes rhétoriques aux images afin de voir comment on a envisagé celles-ci comme étant persuasives et comment on a théorisé leur pouvoir de persuasion. Ce cours rend compte de cette idée de persuasion par rapport aux images, qu’on en ait traité par le discours ou par l’image même, dans une discussion allant de Platon à William Wordsworth, de Roland Barthes à Paul Messaris, de la métaphysique à l’idolâtrie.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.078 | 0.023 |
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".