Ethnolinguistic Vitality, Identity and Power: Investment in SLA
Bibliographic record
Abstract
This article concerns the relationship between social context, identity and intergroup relations. It reviews early formulations pertaining to contextual influences and proceeds to examine vitality theory, specifically, ethnolinguistic vitality (EV). The ensuing discussion considers objective aspects of EV such as the demographic representation of a group and delineates multiple influences modulating their impact on intergroup relations. Subjective formulations of EV focusing on how it is perceived are then examined in view of their interaction with intergroup issues such as ideologies, the formation of networks and language loss. While acknowledging the wide conceptual girth of EV, a central intergroup issue remains communication. Following through, issues pertaining to language acquisition are therefore scrutinized as they relate to EV, linguistic identification, and motivation. A parallel is drawn with Bourdeusian approaches, which orient the analysis toward the dynamics of power and investment. Specifically, attention is directed to ideological and identity processes underpinning the learner’s involvement in the learning task. The paper concludes with a discussion of the relevance of the concept of global English as well as the necessity for a framework of contextual factors reaching beyond intrapsychic constructs.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".