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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".