Bibliographic record
Abstract
The title, and inspiration, for this talk is drawn from a confession of Daniel Dennett at the start of one of his books, in which he writes that in order to get people to think seriously about ideas he cannot use formal argument, because people will not be swayed by that. He has to use "more artful methods"; he has to "tell a story." The contrast between formal argument and story as methods of persuasion is suggestive and worth exploring. But in shifting attention on to the narrative as Dennett does, some interesting questions are encouraged: What is the persuasive nature of narrative? And how do narratives address audiences argumentatively? To provide responses to these questions, I take up some cases of narratives that have been used to persuasive effect. I then place these analyses within a larger project that has been occupying me: describing the nature and power of the cognitive environments in which we interact. This in turn allows me to discuss various devices with both narrative and argumentative import, like allusions and memes, the second of which was adopted by Dennett.
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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".