Cross-cultural Dynamics in Community Interpreting. Troubleshooting
Bibliographic record
Abstract
This paper, challenging the traditional maxim of the interpreter’s ‘neutrality’ and concepts of ‘equivalence’, regards the community interpreter as an active (“meaning-creating”) agent in the cross-cultural interpreting encounter . Due to the inter-cultural context of the exchange (i.e. often people from vastly different cultural and linguistic backgrounds), community interpreting in particular has had to confront the question of cultural difference between interlocutors at the same time having to fit this into the traditional ideal of “translating everything sender says”. With the rapid increase of immigration in Italy (a new target for emigration), ethnic minorities who do not speak Italian are finding it increasingly difficult to make their voice heard. If speaking in and being understood in one’s own language is a fundamental prerequisite for full civil rights, this situation does not guarantee equal access to statutory services. Compared to many other countries, Italy does not have sufficiently developed infrastructures to provide adequate benefits and services for the new immigrants. Although some work is being done (mainly NGO’s), much still remains, especially for what concerns raising awareness among and training service providers, a situation which the present paper will discuss using experience from countries such as Australia, Canada and the UK as comparative material, exploring how responsibility can be shared with the service provider as a culture-sensitive exchange partner. The paper presents the results of a survey conducted by the author in several Italian provinces which throw light not only on the issue of ‘neutrality’ as such, but how both service providers and community interpreters see themselves in terms of ‘being neutral’; I believe that there are inherent contradictions here between ideal and practice, especially among the service providers (i.e. insisting on ‘neutrality’ but expecting effective cross-cultural communication and active participation from the interpreter), perhaps due also to the ambiguity inherent in the word itself.
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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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.030 | 0.047 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".