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Record W7032770061

The others among us : how experience informs post-secondary faculty's preparedness for cultural diversity in the French linguistic minority classrooms of Manitoba

2006· dissertation· en· W7032770061 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2006
Typedissertation
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsCultural diversityCultural competenceLinguistic diversityExperiential learningPreparednessDiversity (politics)Identity (music)Cultural identitySet (abstract data type)Ethnography
DOInot available

Abstract

fetched live from OpenAlex

Due to the ten-fold increase in international students matched with a stronger participation of French immersion graduates in the last eight years, the College universitaire de Saint-Boniface now includes a wider diversity of Francophones in its learning community. This thesis set out to identify the awareness of cultural diversity and to examine the education, training and preparation of post-secondary faculty members at Western Canada's oldest educational institution. The approach was inductive, qualitative and phenomenological, using an interview method. Tacit knowledge of the experiences of 13 interviewed faculty members was drawn out from stories as well as those shared by the teacher/researcher. Professors and instructors demonstrated awareness of the new cultural diversity through experiential learning experiences both formal and informal. Many stated that their comfort and ease of working with a heterogeneous group was because of their own identity as a cultural and linguistic minority within Manitoba.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.471
Threshold uncertainty score0.947

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.007
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.336
Teacher spread0.293 · 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
Published2006
Admission routes1
Has abstractyes

Explore more

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