Ethnolinguistic Orientation and Language Variation: Measuring and Archiving Ethnolinguistic Vitality, Attitudes, and Identity
Bibliographic record
Abstract
Abstract Sociolinguists and social psychologists have long been interested in how language variation is associated with social psychological variables, including people's beliefs about and attitudes toward languages and their speakers, as well as their feelings of affiliation with ethnolinguistic groups, and there is a growing interest in archiving such information along with sociolinguistic data for subsequent research. With this end in mind, we suggest some brief, quantitative indices that might be appropriate and useful for documenting social psychological variables for contemporary and future purposes. The first construct considered is ethnolinguistic vitality , which refers to those characteristics that make a language group likely to behave as an active collective entity in language contact situations. The second is language attitudes , which refer to the feelings and beliefs that people hold with regard to their own and others' languages and the associated language community/ies. The third is ethnolinguistic identity , which refers to the manner and extent to which individuals define themselves as members of an ethnolinguistic group. Although we maintain that more extensive, detailed coding should be included in sociolinguistic archives, we suggest that these three sets of indices should be minimally included in a battery to assess a speaker's ethnolinguistic orientation.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".