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Record W2589753137

The attitudes of Québécois millennials towards English loanwords in Quebec French

2016· dissertation· en· W2589753137 on OpenAlexaboutno aff

Bibliographic record

VenueThinkTech (Texas Tech University) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicLinguistic and Sociocultural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAdvertisingPsychologyPolitical scienceBusiness
DOInot available

Abstract

fetched live from OpenAlex

In sociolinguistics, language attitude studies are a central part of research done in this field. Quebec, being Canada’s only province with a francophone majority, has proved to be a fascinating place for language attitude studies. This thesis is an investigation into the attitudes of Québécois Millennials towards English loanwords in Quebec French. The first chapter of the thesis provides context for the researcher’s own research. It provides a brief overview of the history of the province and explains some of the current social, linguistic, and political events occurring in the province. It also provides an introduction to language attitude research and summarizes previous language attitude researcher conducted in Quebec. The second chapter focuses on the creation of the survey and the selection of participants. The results of the study are presented in this chapter. In the third chapter, the results are analyzed and compared to the historical attitudes towards English loanwords. There is also some analysis of the results with regard towards current events in Quebec.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.012
GPT teacher head0.264
Teacher spread0.251 · 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 designQualitative
Domainnot available
GenreEmpirical

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
Published2016
Admission routes1
Has abstractyes

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