Bibliographic record
Abstract
The development of linguistics, applied linguistics and the language teaching in the last fifty years has been dominated by the studies investigating the English language. This situation is beginning to change as “the success of modernism in integrating all the communities into the global whole has created greater visibility for the local” (Canagarajah 2005). The globalization of the use of English as the language of international communication encounters its opposite trend in the growing interest towards local languages “to resist the colonizing thrust of English” (Canagarajah 2006, 586). Linguistic research becomes increasingly more enriched by explorations of languages other than English coming into the forefront of academic discussions. As pointed out – quite paradoxically in a collection of papers on English-language teaching – “among the darkness of the ‘English only’ movement and the destruction resulting from the hegemony of English, there is a faint ray of hope” (Hall and Eggington 2000, xiii). Among these signs of hope in North America is the first national conference on heritage languages in America held in 1999 (Hall and Eggington 2000), and the establishment of the Heritage Language Journal in 2003. Largely due to the growth of Russian-speaking communities around the world in the last few decades, Russian in particular is getting more involved in the advanced linguistic, neurolinguistic, psycholinguistic and sociolinguistic research and related studies (e.g., Baerman 2011; Goddard 2011; Xiang et al. 2011).
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.429 | 0.373 |
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".