Plurilingual and pluricultural competence (PPC) scale: the inseparability of language and culture
Bibliographic record
Abstract
As multi/plurilingual research advances understandings of plurilingual speakers’ fluid language use, particularly in multilingual settings, new research methods and pedagogical orientations that address this complex phenomenon are needed. The present study considered the development, reliability, and validity of the Plurilingual and Pluricultural Competence (PPC) scale. Informed by sociolinguistics theories in educational linguistics, including plurilingualism and translanguaging, the PPC scale had its content validated by researchers, language teachers and learners. It was then implemented with 379 plurilingual speakers in two multilingual cities in Canada: 129 in Toronto and 250 in Montréal. Exploratory factor analysis examined the factors in the scale and whether PPC referred to language and culture as separate dimensions or, as theoretically suggested, a unidimensional construct. Results reveal PPC as one construct, suggesting that language and culture are interrelated. With 22 items on a 4-point Likert scale, the PPC scale is a new instrument that can be used in future multi/plurilingual research and pedagogy. Its significance lies in that the scale can gather overall trends among plurilinguals’ PPC levels, which can have implications for language education, curriculum and policy. Recommendations for future use are discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".