Corpora in Language Learning, Translation and Research
Bibliographic record
Abstract
In the realm of modern linguistic studies, corpora play a foundational role that extends across diverse disciplines and applications. These extensive language resources are not merely repositories of linguistic data but serve as indispensable tools for language learners, translators, and researchers alike. The articles compiled in this conference proceedings delve into interconnected themes such as corpora, AI technologies, language education, and cultural translation. Together, they celebrate the dynamic synergy between language, technology, and culture in modern linguistic research. Corpus-driven studies shed light on language use and cultural expressions, revealing how languages adapt within diverse cultural contexts. The task of cultural translation highlights the complexities of conveying cultural nuances across languages and cultures. By exploring language corpora, educators can leverage innovative strategies to enhance language proficiency among learners, thus influencing language pedagogy and curriculum design, and individuals can gain deeper insights into language structure, usage variations, and cultural expressions. Through an exploration of the development and utilization of both general and specialized corpora, the selected collection of articles seeks to enrich our understanding of language and its multifaceted dimensions in today’s rapidly evolving global context.
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 | 0.036 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.034 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".