Glocal Language Awareness through Participatory Linguistic Landscape Research
Bibliographic record
Abstract
This article will explore the local experience of language awareness, scholastic linguistic identity and language ideologies through a scholastic linguistic landscape (schoolscape) (Brown, 2012) study in three schools in Canada where French was the language of instruction. Glocality is an especially useful frame for linguistic landscape studies (Manan et al., 2017) and has been used to look deeper into youth identities (Grixti, 2008). Photographic images of each school and photo-elicitation interviews with 37 students were used to qualitatively analyze the visible, written language found on the school walls of secondary schools offering three different French instructional programs. Glocality is used to draw the connections between the local schoolscapes and the global themes of language ideologies, scholastic linguistic identity and language awareness. Involving students in linguistic landscape research results in discussions surrounding linguistic diversity and can lead to multilingual language awareness. At the same time, such a practise can result in incidental language learning. The results showed that students were aware of the importance of their schoolscape as a representation of national language ideologies, as a symbol of their school’s linguistic identity and as a vehicle for promoting language use and awareness. Although the findings are local, the insights gleaned from the students are relevant to a global audience interested in language learning and multilingualism. Particularly, student perspectives and participation in analysis offer a unique contribution to linguistic landscape research and educational research in general.
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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 | 0.019 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| 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".