Bibliographic record
Abstract
The origin of economics of language as a discipline is often credited to prominent economist Jacob Marschak (1965), whose interest in languages was perhaps aided by his command of ten languages. Marshack was the first to introduce explicitly the concept of costs and benefits into linguistic analysis. Some other early contributions (for example, Pool, 1972; Breton, 1978; McManus et al., 1978; Grenier, 1984) notwithstanding, the impact of language on social, political and economic outcomes was mainly the territory of linguists and sociolinguists, political scientists, anthropologists and psychologists. Vaillancourt’s (1982/1983) paper ‘The Economics of Language and Language Planning’ contains 37 references of which more than half were concerned with Quebec’s linguistic problems. In his conclusion, he notes that ‘[t]he main goal of this paper was to review the literature on the economics of language and of language planning so as to provide the reader with an overview of its main findings. To the author’s knowledge that literature, at least in English and French, deals almost exclusively with the case of Quebec. If this is correct, then this paper is a fairly complete survey of it.’ Though this is probably not fully correct, it shows that the literature on language and economics was not quite extensive, as is also evident from Lamberton’s (2002) collection of existing papers. In their important paper Selten and Pool (1991) quote 12 papers only, of which seven are concerned with Quebec (six are written in French and one in English). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.503 | 0.262 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".