Bibliographic record
Abstract
Abstract The impact of neoliberalism on language education has recently attracted scholars' attention. Linguistic entrepreneurship is a conceptual lens through which neoliberal implications for language learning and use can be investigated. This commentary offers comments on common threads of themes running through the four articles in this special issue. While neoliberal ideas provide people with hopes and desires to socioeconomically succeed through management of their linguistic resources, the neoliberal system reproduces inequalities for language learners, teachers, and users as well as for multiple languages. However, the perceived superior status of English that often serves as the foundation for linguistic entrepreneurship is considered to be a social imagination, given the complexity of global geopolitics and the multiple directions of global human mobility. Also, the neoliberal engagement with linguistic entrepreneurship-such as commodified language learning or writing in English for academic publication-often deviates from the genuine aims of learning and research. Such deviation also applies to our own scholarly activities. This recognition encourages us to explore how subversive actions can be made possible for not only language learners/users but also researchers ourselves.
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 | 0.052 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.035 | 0.062 |
| Scholarly communication | 0.030 | 0.023 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.030 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 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".