Bibliographic record
Abstract
The changing demographics of higher education are bringing the teaching of English and the teaching of foreign languages closer together. For an increasing number of students, English is a foreign, a second, an international, or a global language, not the language of a unitary mother tongue and culture. Increasingly, students of French, German, or Spanish are learning a foreign language on the background of experiences of migrations, displacements, and expatriations but also on the background of multilingual and multicultural experiences. The typical language learner is, for example, a Nigerian with a Canadian passport learning German at the University of Texas, or a Czech citizen with a knowledge of English, German, and French enrolled in a Japanese class at the University of California, Berkeley. The common denominator among language learners is their interest in language in all its manifestations: literary and nonliterary, academic and nonacademic, as a mode of thought, as a mode of action, and as a symbol of identity. At UC Berkeley, the current success of courses with titles like Language, Mind, and Society; Language in Discourse; Language and Power; and Language and Identity—as they are offered by English programs, foreign language programs, linguistics departments, or schools of education—is a sign of a renewed interest in the way language expresses, creates, and manipulates “alien wisdoms” through discourse.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".