REVIEW: LANE-MERCIER, G., MERKLE, D. and KOUSTAS, J. (eds.), Minority Languages, National Languages, and Official Language Policies, Montreal and Kingston: McGill-Queen’s University Press, 2018, 360 pp.
Bibliographic record
Abstract
Multilingualism, as a fact of life across all continents (Maher 2017), has turned into a new area of research in the field of sociolinguistics.This area of research interwoven with the field of language policy (Lo Bianco 2010) has been accompanied by the publication of a growing number of significant works in recent years.Among them, Minority Languages, National Languages, and Official Language Policies can be introduced as an authoritative source of information which critically deals with the topic.Organized around an Introduction, eleven chapters headed by six intersecting parts, and an Afterword, the volume mainly attempts to answer this question: "to what degree and in what ways have official multilingualism and multiculturalism policies actually succeeded in attaining their goals?"(p.12).Doing so, its contributors attempted to examine the implementation of official policies of multilingualism at different geographical levels through a series of case studies.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".