"Inte antingen eller, utan mycket mer en mosaik av möjligheter" : Svenskan som EU-språk 20 år senare
Bibliographic record
Abstract
Sweden joined the European Union in 1995, and thus, in 2020, has been a member of the union for a quarter of a century. Entering the EU was a major political event in the history of Sweden, and it had consequences for many spheres of society, including linguistic matters. Membership meant that Swedish became one of the official languages of the EU, and hence a part of the union’s large translation and interpreting services. But did that mean that Swedish representatives could use Swedish when working in various EU bodies? And if not – what languages were used instead? Did English suffice, or could only polyglots represent Sweden in the EU? To answer such questions, in 1999 I conducted surveys with Swedish MEPs, with delegates of the Economic and Social Committee and of the Committee of the Regions and finally with civil servants working at the Swedish Government Offices, regarding their use of different languages in various settings. I have now repeated these surveys, in order to see what has happened during the two decades that have passed since I did my first inquiry. Many things have changed in the course of this time, both within Sweden and within the EU, and it is interesting to compare the situation now and then. This time around, I also supplemented the surveys with interviews with translators and interpreters working in various EU bodies in Brussels and Luxemburg. The results show that the use of English has generally increased. This holds for both reading and writing. When speaking, too, the Swedish delegates often employ English. Only in the most formal setting, i.e., the plenaries, is Swedish the most frequently used language. With respect to interpretation, however, I found a small upturn of the delegates’ choosing to listen to Swedish. The increased use of English in most settings in the EU institutions furthermore means that the difference between the politically elected representatives and the civil servants – where my earlier study showed a much higher usage of English in the latter group – has now disappeared. Especially in the interviews with the interpreters, it is suggested that the Swedish delegates today have a more strategic and pragmatic approach to the choice of language and in a more conscious way vary what language to employ according to the situation, as compared to the situation 20 years ago when there was more of an either-or attitude regarding this. The results of the surveys give some support for this, but this question should be investigated further. However, if true, this might be a reflection of a more common societal trend where switching and even mixing languages according to pragmatic considerations has become a less controversial phenomenon.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".