University administrators as forced language policy agents. An institutional ethnography of parallel language strategy and practices at the University of Copenhagen
Bibliographic record
Abstract
Nation states increasingly assign the responsibility for meeting the global competitiveness agenda to the universities themselves [Cirius, 2009, Mobilitetsstatistik for de videregaaende uddannelser 2007/08 [Mobility statistics for higher education 2007/08]]. In Denmark, universities that have introduced English as an instrument to facilitate internationalisation are called post-national(-ising) [Mortensen & Haberland, 2012, English: The new Latin of academia? Danish universities as a case. International Journal of the Sociology of Language, 216, 175–197]. The present article questions this assumption by outlining results from an institutional ethnographic study of internationalisation at the University of Copenhagen, where national agendas like the preservation of the Danish workplace culture and developing and protecting the status of Danish are very much present. In line with authors who have analysed internationalisation at Danish universities as an uneven and differentiated process due to the counter discourse of immigration prevailing on the Danish labour market [Valentin, 2012, Caught between internationalization and immigration. Learning and Teaching, 5(3), 56–74; Mosneaga & Agergaard, 2012, Agents of internationalisation? Danish universities’ practices for attracting international students. Globalisation, Societies and Education, 10(4), 519–538], my study reveals that internationalisation at a national university in Denmark is a contested field where the conflicting language regimes [Cardinal & Sonntag, 2015, State traditions and language regimes: Conceptualizing language policy choices. In L. Cardinal & S. Sonntag (Eds.), State traditions and language regimes (pp. 3–28). McGill-Queen’s University Press] of internationalisation (favouring English) and immigration (favouring Danish) clash, complicating the linguistic organisation at UCPH [Tange, 2012, Organising language at the international university: Three principles of linguistic organization. Journal of Multilingual and Multicultural Development, 33(3), 287–300]. While using English was regarded as the primary means of solving internationalisation-related challenges by the Danish staff, for the foreign staff, English obscured rather than facilitated their understanding of the Danish workplace culture and university administration, which was their primary internationalisation-related concern.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.031 |
| Scholarly communication | 0.027 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".