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Record W4400094790 · doi:10.4324/9781003401995-10

The Polish language and culture maintenance in Canada “scented with resin”

2024· book-chapter· en· W4400094790 on OpenAlexaboutno aff
Joanna Lustański, Magda Stroińska

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsHistoryPhilosophy

Abstract

fetched live from OpenAlex

Polish immigration to Canada began after partitions and continues in waves until today. Polish Canadians currently comprise 0.6% of the population (over 200,000), ranking as the 13th largest ethnic group. Their number is slowly declining as the last major wave of immigration occurred around 1981 while other ethnic groups increased their representation. Poles assimilated well but consistently strived to preserve their linguistic, cultural, and religious identity. Unlike some other host countries, Canada gives immigrants a certain freedom to remain loyal to their customs and traditions, be it religion or language. Immigrants are encouraged to learn the country’s language and norms, but Canadian multiculturalism values heritage language retention and cultural diversity. Provincial governments support heritage language education in schools, and some universities offer Polish as an elective or part of their program. The knowledge of Polish among second and third-generation immigrants depends on parental attitudes and efforts to preserve the language. Children in multilingual environments effortlessly learn multiple languages, including their parents’ native tongue. However, even those who grew up speaking Polish may struggle with reading and writing in Polish. Heritage language courses at the university level cater to this target population. Given Canada’s multicultural landscape, knowing another language, especially in professional and business settings like healthcare, the legal system, and marketing, is advantageous.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.573
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.168
Teacher spread0.161 · 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 teacher head, 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

Citations2
Published2024
Admission routes1
Has abstractyes

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