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The Politics of Language

2001· book· en· W3023432645 on OpenAlexaboutno aff
Carol L. Schmid

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicGender Studies in Language
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsPolitical scienceLinguisticsSociologyPhilosophyLaw

Abstract

fetched live from OpenAlex

Abstract Important aspects of the history of language in the United States remain shrouded in myth and legend. The notion of “one nation, one language” is part of the idealized history of the United States, although in its short history it has probably been host to more bilingual people than any other country in the world. Language is more than a means of communication. It brings into play an entire range of experiences and attitudes toward life. Furthermore, language is a potent symbolic issue because it links power and political claims of ownership with psychological demands for group worth. How people belonging to different language and cultural communities live together in the same political community and how political and structural tensions arise to divide them along language lines, are questions addressed in The Politics of Language. This book analyzes the historical background and recent controversy over language in the United States and compares it to two official multilingual societies: Canada and Switzerland. It’s accessibility as a survey of this topic makes it ideal for courses in linguistics, political science, and sociology.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.016
Scholarly communication0.0080.005
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.022
GPT teacher head0.328
Teacher spread0.306 · 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 designTheoretical or conceptual
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

Citations174
Published2001
Admission routes1
Has abstractyes

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Same topicGender Studies in LanguageFrench-language works237,207