Verso una semiotica della manipolazione
Bibliographic record
Abstract
The expression “manipulation” reveals some sober but eloquent hints of its extension if we consider its main definition as “manual reworking”. This fundamental operation inspires the other meanings that globally recur to the changing reality of alteration: the adaptation and composition of heterogeneous elements, interventions carried out through techniques aimed at selecting useful characteristics for a biological structure, and, finally, the tendentious reworking of the truth through the altered or partial presentation of data and news in order to maneuver according to one’s own ends and interests in political and moral orientations. Of these extensions of manipulation, the latter attracts our attention most recently because of its spread in communication strategies with nefarious effects, the signs of which we recognize more and more.However, an examination of the advantageous aspects of manipulation can only benefit the study of the phenomenon. Detienne et Vernant (1974) identify metis (cunning) as a mental category that provides the archetype of ‘good manipulation’. Human processing, Vernant explains, has an intelligent purpose, in contrast to natural processes that are random and unpredictable. Craft activities, for example, involve metis. But its scope is immense: it practically concerns the cultural universe in all its aspects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".