Bibliographic record
Abstract
Abstract Language is a social behavior and a key aspect of social interaction. Language is ubiquitous and usually occurs with other human behaviors across diverse contexts. Thus, it is difficult to study it in isolation. This difficulty may be why most, albeit not all, social psychologists tend to neglect language, in spite of the prominence of language in early 20th century social psychology and the presence of numerous handbooks and reviews of this area. Language use has implications for many social psychological processes, and, given its role in daily social life, it is important to understand its social underpinnings. The field of language and social psychology highlights the relationship between language and communication and foregrounds the differences between the social-psychological and communication approaches. One central issue is bilingualism and the relationships among language, identity, and culture. Another is methodology, where social psychologists have tended to choose experimental and survey strategies to look at language (not always to the best advantage). This century has seen the development of new technologies that allow us to look at language on a large scale and in rich detail and that have the potential to transform this research. In part as a consequence, in the early 21st century there are many new topics emerging in language and social psychology that help to set a new agenda for future research.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".