Bibliographic record
Abstract
Social psychology is conventionally defined as the scientific study of how the actual or imagined presence of others influences an individual's thoughts, feelings, and behavior. The social psychology of language (SPL) concentrates on the role of language in the dynamics between individuals and their social world; language use is argued to affect and be affected by both psychological and social variables. Scholarship in SPL has incorporated aspects of the broader discipline, including a theory‐ or variable‐driven approach, along with its preferred quantitative methods, particularly survey techniques and laboratory experiments (→ Survey; Experimental Design). Unlike mainstream social psychology, SPL has also welcomed contributions from other disciplines, including communication studies, sociolinguistics, anthropology, and other disciplines. Correspondingly, constructivist perspectives and qualitative methods, which are better recognized in these other disciplines, have found a receptive community in SPL (→ Qualitative Methodology; Constructivism). Furthermore, SPL scholars herald from around the world, and different “schools” of scholarly activity have developed in many regions, including Canada, the United Kingdom and Europe, Australasia, and the United States. Because of the range of perspectives that inform SPL, it cannot be said that any single paradigm characterizes this field of inquiry; indeed, a hallmark of research in this area is its variety of theoretical and methodological approaches.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".