Minority Language Learning and Use: Can Self-Determination Counter Social Determinism?
Bibliographic record
Abstract
To what extent is minority language use in society imposed by social determinism, a force acting on individuals based on the language group's relative vitality in terms of demography, institutional support, and status? Can social determinism be countered by the force of self-determination sustained by group members’ personal autonomy, critical consciousness, and strong engaged integrated identity? These questions are addressed by testing a revised Self-determination and ethnolinguistic development (SED) model, using structural equation modeling. This model specifies how three categories of language socialization (enculturation, personal autonomization, critical consciousness-raising) mediate between objective ethnolinguistic vitality (EV) and four psycholinguistic constructs (engaged integrated identity, community engagement, linguistic competencies, subjective EV) in the prediction of minority language use. Results on a large sample of French Canadian students in different EV settings strongly support the SED model and show that social determinism can be at least moderately countered by psycholinguistic constructs that increase individual self-determination.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".