Embodying discourse analysis: Lessons learned about epistemic and ontological psychologies
Bibliographic record
Abstract
Responding to critiques of ‘Including social discourses and experience in research on refugees, race, and ethnicity’, and providing suggestions for future work in discursive psychology, this article expands upon the complex and dynamic character of research in socioculturally informed social science. Through a better understanding of experience and an awareness of broader social discourse, one is able to consider verisimilar ontologies, which are fundamentally socio-linguistic phenomena. It is important to understand the value in using discursive psychology’s analytical practices; however, an awareness of the need to expand upon such practices is necessary in order to better understand how experience is cultivated in dynamic and rhythmic co-regulated social constitution. Future research should endeavor to develop techniques that are not necessarily formulaic, but are still examinable for the purposes of determining good versus bad work.
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 | 0.046 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.089 |
| Scholarly communication | 0.022 | 0.052 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".