Interprètes, contextes, situations : l’interprétation comme acte politique
Bibliographic record
Abstract
Interpreting is meant to meet the needs for mutual understanding arising within a community at a national or international level. These needs derive from the various settings and situations that characterize society, as well as people’s lives, and are closely linked to the life and organization of the polis (the State). Interpreting therefore belongs to the political and social spheres; as such it is a political act. In order to analyze the conditions underlying the recourse to interpreting, a superordinate and a subordinate level may be identified. The former refers to the political, historical, and spatio-temporal situation in which interlingual communication needs arise and its impact on interpreting services. The latter refers to the interpreter as an individual who “chooses” what to interpret and for whom, and whose activity enables those communication needs to be met. In order to highlight the intertwined features of the two levels, as well as the political value of the interpreting act, this paper focuses on two areas: the legal field (with exclusive reference to Italy) and interpreting in conflict zones. Given their contexts and the impact they have on society and people’s lives, including the interpreter’s, these two areas can be seen as emblematic insofar as they forcefully reveal the political nature of both the interpreter’s role and the interpreting act.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.054 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".