Systems, features, figures: Approaches to language and class vs. language and race
Bibliographic record
Abstract
Abstract This paper contrasts different approaches taken in research on language and race vs. language and class. It looks at the timescales, units of analysis, and phenomena that have drawn scholars’ attention, and considers how each subfield approaches the study of language and inequality. 본고는 언어와 인종주의 연구, 언어와 사회계층 연구의 두 분야에서 쓰이는 다양한 이론적 접근들을 비교·분석 한다. 본고는 기존연구들에서 쓰여진 시공간적 접근방법, 연구분석 단위와 분석방법 및 연구 현상을 면밀하게 검토하며 이러한 이론적 접근들을 언어와 사회 불평등 연구분야에 어떻게 적용시킬 수 있는지 알아본다.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".