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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.039 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".