The contribution of exterior schoolscapes to neighbourhoods: a linguistic landscape analysis during COVID-19 school closures
Bibliographic record
Abstract
Within neighbourhoods, exterior signage of schools is part of the overall linguistic landscape. As community members pass by, these signs contribute to the languages they read. Additionally, signs serve functions in keeping with their visibility. Early in the COVID-19 pandemic in Canada (March – August 2020) school buildings were closed to students, yet exterior signage remained present for passersby. In this study, how the languages and functions of these schoolscapes contributed to the overall linguistic landscape during this time period was explored. We photographed 1452 signs from Bilingual Programme schools and their neighbourhoods. We analysed the languages and Halladayan functions of the signs to investigate how school signs contributed to the overall linguistic landscape. English-only signs were predominant, and most signs served as regulatory (rule-enforcing), including the small sample of signs that were specific to the COVID-19 pandemic. We argue that schoolscapes influenced the multilingualism of the neighbourhoods through their languages of instruction. Schoolscapes mainly functioned in regulating behaviour of passersby and communicating that their spaces were largely closed to them as outsiders. These findings confirm the important contribution of schoolscapes in the ecology of the linguistic landscape.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".