Bibliographic record
Abstract
ABSTRACT This article locates Norton's foundational work on identity and investment within the social turn of applied linguistics. It discusses its historical impetus and theoretical anchors, and it illustrates how these ideas have been taken up in recent scholarship. In response to the demands of the new world order, spurred by technology and characterized by mobility, it proposes a comprehensive model of investment, which occurs at the intersection of identity, ideology, and capital. The model recognizes that the spaces in which language acquisition and socialization take place have become increasingly deterritorialized and unbounded, and the systemic patterns of control more invisible. This calls for new questions, analyses, and theories of identity. The model addresses the needs of learners who navigate their way through online and offline contexts and perform identities that have become more fluid and complex. As such, it proposes a more comprehensive and critical examination of the relationship between identity, investment, and language learning. Drawing on two case studies of a female language learner in rural Uganda and a male language learner in urban Canada, the model illustrates how structure and agency, operating across time and space, can accord or refuse learners the power to speak.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".