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Oral speech as a marker of Quebec identity

2024· article· en· W4404048079 on OpenAlexaboutno aff
Лина Васильевна Разумова

Bibliographic record

VenueSociolingvistika · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Oral speech is an important element in the study and description of Francophone cultures and literatures. It is often the starting point in thinking about their specificity. The cultures of northern and southern Francophonie contain a special linguistic heritage and a special historical context of oral speech functioning. In conditions of multiculturalism, multilingualism and colonial domination, it acts as the most important marker of national identity. At the same time, oral speech also appears as a kind of universal, marking the starting point for the development of any literature. Quebec poetry of the last third of the 20th century is a multifaceted phenomenon. It is the bearer of important innovations, reflecting the main trends and literary themes characteristic of each era. At the same time, Quebec poetry of the specified period of the 20th century also acts as an expression of a specifically Quebec tradition. The latter can be considered as a collective phenomenon, fitting into the Quebec poetic tradition with its sociocultural dominants, the oral nature of the presentation, as a kind of sound and visual anthology of modern Quebec poetry. It also allows us to reflect the work of a significant number of poets. Within the framework of the article, special attention will be paid to the issue of analyzing Quebec poetry as a form of oral heritage and a means of sociocultural and linguistic identification.

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)0.004
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 score0.045
Threshold uncertainty score0.325

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0100.006
Scholarly communication0.0060.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.017
GPT teacher head0.308
Teacher spread0.291 · 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
Published2024
Admission routes1
Has abstractyes

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