Bibliographic record
Abstract
In this review article on identity, language learning, and social change, we argue that contemporary poststructuralist theories of language, identity, and power offer new perspectives on language learning and teaching, and have been of considerable interest in our field. We first review poststructuralist theories of language, subjectivity, and positioning and explain sociocultural theories of language learning. We then discuss constructs of investment and imagined communities/imagined identities (Norton Peirce 1995; Norton 1997, 2000, 2001), showing how these have been used by diverse identity researchers. Illustrative examples of studies that investigate how identity categories like race, gender, and sexuality interact with language learning are discussed. Common qualitative research methods used in studies of identity and language learning are presented, and we review the research on identity and language teaching in different regions of the world. We examine how digital technologies may be affecting language learners' identities, and how learner resistance impacts language learning. Recent critiques of research on identity and language learning are explored, and we consider directions for research in an era of increasing globalization. We anticipate that the identities and investments of language learners, as well as their teachers, will continue to generate exciting and innovative research in the future.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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".