MAPPING LINGUISTIC DIVERSITY IN MULTICULTURAL CONTEXTS: DEMOLINGUISTIC PERSPECTIVES
Bibliographic record
Abstract
(Van der Merwe 1989). The term demolinguistics originated among Quebec demographers, probably during the 1970s. Over the last few decades, the field has become an international crossing for demography and linguistics; the same holds for geolinguistics as the crossing for geography and linguistics. More recent approaches have been explored in terms of linguistic landscaping. Whereas geolinguistic and demolinguistic studies tend to focus on the spatial and temporal distribution and vitality of languages in the private domain of the home, linguistic landscaping has as its focus the public domain in the most literal sense, i.e., in terms of the visibility and distribution of languages on the streets. In this sense, the outcomes of linguistic landscaping research should be read with care: they do not intend to present a faithful mapping of the linguistic make-up of the population in a given place. We will offer cross-national demolinguistic perspectives on languages other than “national” languages. Depending on particular contexts or perspectives, such languages are often referred to as minority languages or dominated languages. Numerical classifications do not necessarily coincide with social classifications. According to the 2001 census outcomes in South Africa, (isi)Zulu is the most widely spoken home language there and English functions commonly as
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".