Bibliographic record
Abstract
THE ISSUE A Proposal for Action: Strategies for Recognizing Heritage Language Competence as a Learning Resource within the Mainstream Classroom JIM CUMMINS, University of Toronto THE COMMENTARIES More than a Silver Bullet: The Role of Chinese as a Heritage Language in the United States SCOTT MCGINNIS, Defense Language Institute, Washington Office The Reemergence of Heritage and Community Language Policy in the U.S. National Spotlight TERRENCE G. WILEY, Arizona State University Positioning Heritage Languages in the United States OFELIA GARCÍA, Teachers College, Columbia University Opening and Filling Up Implementational and Ideological Spaces in Heritage Language Education NANCY H. HORNBERGER, The University of Pennsylvania The Use of Heritage Language: An African Perspective JANINA BRUTT–GRIFFLER, University of York, United Kingdom SINFREE MAKONI, The Pennsylvania State University A European Perspective on Heritage Languages KEES DE BOT, University of Groningen, The Netherlands DURK GORTER, University of Amsterdam & Fryske Akademy, The Netherlands
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.199 | 0.054 |
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".