Local languages and the linguistic landscape: the visibility and role of Sardinian in town entry and street name signs
Bibliographic record
Abstract
Abstract The present study deals with the presence and role of the Sardinian language in the linguistic landscape of sixteen villages in the province of Oristano, Sardinia. Specifically, their entry signs and street name signs were photographed and analysed using both quantitative and qualitative approaches. In the entry signs, Sardinian was found to have a very strong presence, generating a high degree of bilingualism with Italian, as recommended by national and regional language policies. Systematic bilingualism could not, however, be observed in the street name signs, where Italian clearly prevails. Notwithstanding, the local language is visible in around a quarter of all street name signs. Complying with the provisions of national and regional legislations, Sardinian is used in the majority of street signs to recall the historical memory of the communities. Nonetheless, signs could be found where Sardinian is used in parallel bilingual texts to express exactly the same content as Italian, fostering a modern vision of the minority language. The present work shows how top-down language policies can be locally implemented or re-interpreted in the linguistic landscape; moreover, the article sheds light on how street naming can be exploited to influence society’s perception of minority languages and convey messages of local or regional/national identity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".