Bibliographic record
Abstract
In an era of globalization, issues of language diversity have economic and political implications. Transnational labor mobility, trade, social inclusion of migrants, democracy in multilingual countries, and companies’ international competitiveness all have a linguistic dimension; yet economists in general do not include language as a variable in their research. This volume demonstrates that the application of rigorous economic theories and research methods to issues of language policy yields valuable insights. The contributors offer both theoretical and empirical analyses of such topics as the impact of language diversity on economic outcomes, the distributive effects of policy regarding official languages, the individual welfare consequences of bilingualism, and the link between language and national identity. Their research is based on data from countries including Canada, India, Kazakhstan, and Indonesia and from the regions of Central America, Europe, and Sub-Saharan Africa. Theoretical models are explained intuitively for the nonspecialist. The relationships among linguistic variables, inequality, and the economy are approached from different perspectives, including economics, sociolinguistics, and political science. For this reason, the book offers a substantive contribution to interdisciplinary work on languages in society and language policy, proposing a common framework for a shared research area Contributors: Alisher Aldashev, Katalin Buzási, Ramon Caminal, Alexander M. Danzer, Maxime Leblanc Desgagné, Peter H. Egger, Ainhoa Aparicio Fenoll, Michele Gazzola, Victor Ginsburgh, Gilles Grenier, François Grin, Zoe Kuehn, Andrea Lassmann, Stephen May, Serge Nadeau, Suzanne Romaine, Selma K. Sonntag, Stefan Sperlich, José-Ramón Uriarte, François Vaillancourt, Shlomo Weber, Bengt-Arne Wickström, Lauren Zentz
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".