Bibliographic record
Abstract
Abstract Acculturation is the process of bidirectional change that occurs when two ethnolinguistic groups come in sustained contact with one another. Acculturation usually occurs between groups of unequal power, status, and demographic background. At stake for the unity of multilingual states are intergroup relations between language minorities and majorities that yield harmonious to conflictual outcomes. The Interactive Acculturation Model (IAM) is adapted to intergroup relations between language communities in four parts. The first part of the model provides an overview of the ethnolinguistic vitality framework accounting for the strength of minority/majority language communities as they struggle to gain the institutional support they need to develop as distinctive and thriving language communities. The second part of the IAM offers an analysis of the pluralist, civic, assimilationist, and exclusionist ideologies that underpin language policies regulating the co-existence of minority/majority language communities. The third part examines the acculturation orientations endorsed by majority and minority language group speakers. The fourth part of the IAM proposes that the interaction of majority and minority acculturation orientations yield intergroup communication outcomes that may range from harmonious, problematic, to conflictual. Taken together, the IAM model offers a conceptual tool for analyzing the fate of linguistic minorities as they seek to survive in the dominant majority group environments of post-modern globalizing states.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".