Bibliographic record
Abstract
ABSTRACT This year, linguistic and semiotic anthropologists are responding in particular to multiple sources of uncertainty and discuss a growing sense of crisis and anxiety across several different settings and contexts. The politics of truth and the uncertainty about the future, as well as the shifts due to new communicative technologies, are well represented in this last year of publications. To consider this collective response, this article reviews published work in this field in three parts: (1) the semiotic interplay of certainty and uncertainty, especially in relation to evidence and agency; (2) the remediation of semiosis across media technologies and infrastructures; and (3) the semiosis of the state‐citizen divide. More generally, this review considers how linguistic/semiotic anthropologists are renewing foundational concepts and approaches at the same time as they express a strong desire to turn their expertise into a more public form of political agency. This process of renewal is leading to the emergence of new agendas. [language, semiotics, uncertainty, media, state]
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".