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Record W2563270170

Attitudes Towards Native and Non-native French Speaking Teachers in Ontario

2013· dissertation· en· W2563270170 on OpenAlexaboutno aff
Sarah Kipp-Ferguson

Bibliographic record

VenueTSpace (University of Toronto) · 2013
Typedissertation
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
FundersOffice of International Science and Engineering
KeywordsNative americanMathematics educationPedagogyPolitical sciencePsychologySociologyAnthropology
DOInot available

Abstract

fetched live from OpenAlex

Through the implementation of a closed and open-item questionnaire, parents’ (N=40) perceptions of and attitudes toward native and non-native French-speaking teachers (NFSTs and Non-NFSTs) of French as a Second Language in the Greater Toronto Area were investigated. Participants defined the native French speaker predominantly as someone who learned French as a first language and who learned French in informal environments – namely home and community. Descriptive statistics of 24 Likert-scale items revealed preference for NFSTs to teach oral-aural aspects, the written system of French and form better student relationships. Non-NFSTs were preferred to teach reading, vocabulary, learning strategies and make connections between English and French more salient. Parents stated a variety of strengths and areas needing improvement for both NFSTs and Non-NFSTs, which suggested complimentary and complementary views of these teachers.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.383
Teacher spread0.340 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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