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Record W7010851726

Language and Minority Rights: ethnicity, nationalism and the politics of language (2nd ed).

2011· book· en· W7010851726 on OpenAlexaboutno aff

Bibliographic record

VenueResearchSpace (University of Auckland) · 2011
Typebook
Languageen
FieldEngineering
TopicCivil and Structural Engineering Research
Canadian institutionsnot available
Fundersnot available
KeywordsNationalismSociolinguisticsPoliticsCosmopolitanismIdentity (music)Field (mathematics)Minority languageSociology of language
DOInot available

Abstract

fetched live from OpenAlex

Published in Nov 2011, this is a major revision of my seminal analysis of language rights & education. The book has reconfigured the field by providing an interdisciplinary analysis of language, identity and education alongside a defence of group-based language rights. It draws on sociology, sociolinguistics, politics, education, law, & explores in-depth international examples, including NZ, Wales, Catalonia, Quebec, France and USA. The 1st ed. was described as ‘a book of breathtaking conceptual and geographical scope’ (Ency. of Applied Linguistics). It has been cited over 560 times (Google Sch), shortlisted for the BAAL Book Prize 2002 & awarded an American Library Association Outstanding Title Award 2008. The 2nd ed. has 50,000 wds (40%) of new material. It addresses new theoretical developments over the last decade in sociolinguistics as to whether languages are definable, in sociology & political theory re cosmopolitanism and globalization, and in education re English as the language of mobility.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0060.005
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0150.006

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.

Opus teacher head0.010
GPT teacher head0.220
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations51
Published2011
Admission routes1
Has abstractyes

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