Bibliographic record
Abstract
HOW CAN WE DISTINGUISH between ‘charm’ and ‘charisma’? Where does one end and the other begin? And how might Henry James help us?1 ‘Charm’ and ‘charming’ are not normally critical concepts or tools; yet informally, in everyday conversation and discourse, they feature prominently, even or especially because they do not seem to say very much. But if we look back at the history of the words across time, and the ideas with which they are freighted, they turn out to be of immense significance. They are associated with other words involving magic, with spells and chants, and therefore with the power of art over readers, or perhaps more specifically listeners and viewers. Modest words for us now, so it seems, yet they point us towards ethical and political questions about the power of art to enchant, especially art in performance – in theatre and music, on stage and screen. These are questions that famously worried Plato, and should rightly continue to worry us, in the very different circumstances through which words, images, sights, and sounds now fasten on us.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.010 | 0.013 |
| 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".