Bibliographic record
Abstract
In the previous chapter we considered ‘modelling’ as an artistic process capable of capturing some aspect of a prototype, often but not always through the creation of a reduced-scale stand-in. Use of the idea of ‘capture’ here implies that the prototype is somehow affected by its substitute – and this sounds almost magical. It is a principle (the ‘Law of Similarity’) that lies at the root of Frazer’s account of sympathetic magic: that by making a likeness of something one might gain some power over it. This is taken further by Michael Taussig in his work on mimesis. 1 However, as Taussig underlines, Frazer identified not only a Law of Similarity, but also a Law of Contact or Contagion. That is to say, things that have been in contact may continue to act on each other even when separated. In terms of contagious magic, this is often described as the magic that can be performed using a person’s exuviae, such as fingernails or hair – once in contact with and part of the person, but still capable of being acted upon to gain some power over the person even at a distance. Taussig uses a different example, that of a horse’s hoofprint, required in the magic performed to change the mind of the horse’s owner. 2 The hoofprint is an interesting case because, although it does follow the principle of contact, it is at the same time an image of (part of) the horse. Taussig goes on to argue that ‘in many, if not in the overwhelming majority of cases of magical practices in which the Law of Similarity is important, it is in fact combined with the Law of Contact ’. 3
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.253 | 0.082 |
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".