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Record W2411253106 · doi:10.1057/9780230596047_18

English Summary of Tabouret-Keller’s Postface

2007· book-chapter· en· W2411253106 on OpenAlexaff
Monica Heller

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2007
Typebook-chapter
Languageen
FieldArts and Humanities
TopicHistorical Linguistics and Language Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNeuroscience of multilingualismIdeologyArgument (complex analysis)PoliticsSituatedPower (physics)SociologyEpistemologyPolitical scienceLinguisticsLawPhilosophyPhysics

Abstract

fetched live from OpenAlex

Tabouret-Keller situates our current debates about the political and ideological nature of thinking about bilingualism in a long history. She shows how, at the beginning of the twentieth century, we can already see public debates which alternately argue that bilingualism must be completely abstracted away from politics (the example she gives is an argument that bilingualism can be understood as a purely pedagogical question) or, on the contrary, that it must centrally be treated politically (in her example, bilingual education in central Europe is critiqued as assimilatory). That is, as long as we have talked about it, our own positions have led us either to mask or, on the contrary, to aim to reveal how bilingualism is caught up in relations of power. How that happens, and why, is a matter of conditions on the ground, that is, of the particular ways in which individuals and groups are situated with respect to the construction of nation-states and of their colonies. 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 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)0.003
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: Other
Teacher disagreement score0.150
Threshold uncertainty score0.503

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1500.055

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 head0.030
GPT teacher head0.234
Teacher spread0.203 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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