Promoting Language Learning and Social Inclusion in British and Irish Language Centres
Bibliographic record
Abstract
As part of an integrated approach to promoting quality education, diverse linguistic and cultural backgrounds as well as inclusive modalities must be considered as key indicators and be incorporated into educational programs (Council of Europe, 2020). This paper investigates how British and Irish University Language Centres present their services, facilities, and language courses to university students and the community in general. The main focus of this study is to understand which factors University Language Centres are emphasizing to foster “inward” and “outward” inclusive education for an effective participation in society. For this purpose, the content and language of a corpus of University Language Centre websites are examined in order to understand how universities are working towards a more multilingual, inclusive, and integrated community. In particular, the data collected refer to specific issues which signal the openness of the centres to welcome diversity and provide learners with the skills and tools to enter a new and more respectful world. As part of the study, we explore the way facilities for students with learning differences and sign language courses are signposted in the context of a broader integration (Heslinga & Nevenglosky, 2012).
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".