Bibliographic record
Abstract
In the theoretical and epistemological frameworks of Vygotsky’s cognitive theory and French intellectuals’ written legacy (Cixous, Deleuze, Derrida, Foucault, Kristeva, and Lyotard), the research explores philosophical, psychological, and educational migrations of a second language (L2) learner among cultures and languages in her comprehension and further nativization of an L2 through her comprehension and nativization of the culture of the language. The role of Canadian culture in Canada’s second/additional language education (SLE) is the research focus. In this research, the concept of Canadian culture is interpreted narrowly as literature, music, arts, and history of its people, and broadly as creations of its people. The dissertation consists of 3 parts: Pre-Theory, Theory, and Post-Theory. The Pre-Theory part is built according to the conventional thesis design: introduction, theoretical framework, literature review, research question, methodology, credibility, and significance. Narrative inquiry (Connelly & Clandinin, 2006) as the initial methodology of the research unfolds in innovative ways as literary-philosophical essays in the Theory part, and later as a music-poetry work in the Post-Theory part. The Theory part is a conceptual philosophy-arts piece of writing that develops based on the principle “writing as a method of knowing”. The Post-Theory part is the researcher’s music-poetry work “I-Migrations: Psychedelic Story” that is a practical epitome of her research theory. Based on her own way of learning English, first, as a foreign language (FL) in Russia, and then as an L2 in Canada, the researcher theoretically substantiates her postulate of the underestimated role of Canadian culture, in terms of literature, music, arts, and history in Canada’s SLE and proposes to make Canadian culture an integral part of Canada’s SLE curricula. This research fulfils the gaps in the literature on an older L2 learner’s experience across a lifetime and the inclusion of arts and culture alongside of language learning in SLE. Keywords: second language, second language culture, writing, second language writing, second language education
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".