Bibliographic record
Abstract
This 2-year qualitative study explores the ironies of educational reform in the United States as experienced by three second language learners attending a school attempting to transform itself into a high-performance elementary school in California's Silicon Valley. Drawing on the concept of ‘fast capitalism’ in a globalized economic work order (Gee, Hull, & Lankshear, 1996) and the tools of critical discourse analysis (Fairclough, 1989), the findings from this investigation reveal that the discourses of school reform in the United States visibly and invisibly placed second language learners in new highly vulnerable positions. In what follows I provide an analysis of this vulnerability by relating the experiences of three families and their attempts to enrol and stay enrolled in the school of their choice. Next, I provide a critical analysis of the discourses of reading and writing instruction and of a text produced by a focal student named Alma in this context. This analysis highlights the ways in which classroom literacy practices inadvertently constrained the efforts of second language learners to acquire academic literacies and ultimately legitimated the school's decision to declare Alma ‘not Web material.’ The implications of this study relate to better understanding classroom SLA from a historical, institutional perspective and to supporting teachers and policy makers in addressing the needs of second language learners in a time of rapid social and economic change.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".