doi: 10.3389/fpsyg.2014.01464 Understanding multilingualism and its implications
Bibliographic record
Abstract
The world’s demographics are in a state of flux. Approximately half of the world’s population is bilingual (Grosjean, 2010). Just over half of all Europeans speak a language other than the official language in a given country, and 25 % of them report that they are able to hold a conversation in at least two additional languages (European Commission, 2012, p. 18). Bi- and multilingualism are also the reality in North America. Grosjean (2012) esti-mates that 20 % of Americans are bilingual. In 2011, over 20 % of Canadians reported speaking a mother tongue other than English or French, and the number of Canadians who report being bilin-gual is rising rapidly (Statistics Canada, 2012).While the causes of increased bi- and multilingualism vary, the repercussions of this demographic shift are wide reaching. In August 2013 the Language Research Centre at the University of Calgary brought together a range of experts working on
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.374 | 0.050 |
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".