Conceptualising Linguistic and Cultural Mediation
Bibliographic record
Abstract
This editorial discusses some issues associated with the definition of ‘language and culture mediation’, in order to contextualise the reflections found in this special issue. This expression, rather controversial in some countries, is often used to describe activities of assistance to foreigners, mainly migrants, similar to or coinciding with public service interpreting. A component of mediation is also recognised in the translator’s profession as s/he mediates between the source text and the final readers, and between the relevant cultures. But language and culture mediation can be interpreted more broadly, to refer also to situations of cultural contact involving a process of culture learning and synthesis. In this interpretation, rather than specific professional profiles, it designates various activities and situations involving mediation between cultures, e.g. in tourism communication, or in the promotion of culturally relevant products for export. Hence, it is argued that many problems surrounding the denomination ‘language and culture mediation’ are due to its use in specific contexts, while, if used as a superordinate, this expression can embrace various actions, activities and professional profiles having the common property of granting mutual accessibility to languages and cultures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.067 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.009 |
| 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".