Intercultural communicative competence in a university language centre in Mexico: Teachers' and students' perceptions and practices
Bibliographic record
Abstract
The intercultural dimension of English teaching has been widely acknowledged in policies and curricula but insufficiently investigated in the classroom (Byram, 2014; Baker 2015). In Higher Education (HE), international and intercultural dimensions are expected to be integrated in teaching, research and services. Given the widespread use of English as a lingua franca (ELF) in diverse contexts, one of the key strategies is the teaching and learning of English, together with intercultural and communicative competence (ICC). This thesis investigates the perceptions and practices of intercultural communication and the notion of ICC in two mandatory courses for internationalisation in a Mexican higher education institution (HEI). Data was collected from two rounds of teacher interviews, classroom observations and a focus group, whereas the learners were given a paper-based survey and face-to-face interviews. The teachers considered linguistic competence sufficient for effective communication and ELF resulted an unfamiliar term for most of them. The prevailing model of communication is that of the Anglophone native speaker (NS) mainly from the USA, UK, or Canada. Their teaching practices are characterised by the comparison and contrast of two national cultures and culture teaching is sporadically included. No specific type of knowledge, skills, or attitudes for ICC was overtly promoted in class, considering the global context for HE. The learners considered that language knowledge and some attitudinal elements can contribute to effective communication. For them, English meant the possibility of a better job or a scholarship. They also viewed it as the means to interact with other cultures, although these are not clearly defined. They did not report that these courses had made them more aware of ELF for intercultural communication. The findings of this study suggest that the need for teacher training on notions that are more in line with present hybrid and complex uses and users of English.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".