Factor mobility, efficiency and language discrimination: analysis of four company scenarios in Catalonia
Bibliographic record
Abstract
Amado Alarcón This article presents the results of a case study of companies in Catalonia (Spain, European Union) in which we analyse the problems of efficiency and distribution of resources based on ethno-linguistic criteria. The academic literature, especially that carried out from Quebec, has analysed language demands in the workplace in bilingual contexts indicating that these latter are found to be conditioned by: 1) the language in the consumer markets 2) the language of the technologies used in the work, and 3) the language of the company owners. What we have here, however, is a more complex situation than the bilingual, given the linguistic heterogeneity of the European Union. This complexity increases with the mobility of factors otherwise in place (Economic and Monetary Union) and can be seen in the growing linguistic diversity of companies. In fact, as we shall see in the body of the article, factor mobility places the owners, workers and customers from different language communities in the same
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".