Bibliographic record
Abstract
The 20th century was dominated by a monolingual bias in second language acquisition (SLA) and teaching English to speakers of other languages (TESOL). In the 21st century, SLA has been evolving from a monolingually biased discipline to a multilingually oriented one, by redefining outdated terminology, proposing objective research designs, and advancing new theories, such as the holistic and the dynamic views of bilingualism, and the theory of multicompetence. The multilingual turn in SLA triggered a multilingual turn in TESOL. Most modern multilingual English classrooms are characterized by an increasing plurality of practices and discourses. TESOL can be equitable only when done through a multilingual lens , by incorporating translanguaging , bilingual instructional strategies, teaching for transfer activities that build on students' prior knowledge , identity texts, and a participatory‐transformative curriculum. Multilingual TESOL embraces the linguistic plurality and the cultural diversity that students bring to the classroom and views them as funds of knowledge .
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.000 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".