Bibliographic record
Abstract
Current research into epistemics in social interaction considers the various ways in which participants design their contributions and understand the contributions of others, in relation to a distribution of knowledge that is assumed to preexist. Following the work of John Heritage, this article suggests that a wide range of phenomena within this general domain of interactional organization can be understood through the central ideas of epistemic status and epistemic stance. Epistemic status refers to a distribution of knowledge among participants that is assumed to preexist and to the rights and entitlements that flow from it. Epistemic stance on the other hand refers to the multitude of ways in which a turn at talk can convey through the details of its design the differential knowledge entitlements of speaker, recipient, and other participants. These ideas have been developed, and are articulated here, within a conversation‐analytic framework: an approach to talk and social interaction that adopts a broadly naturalistic perspective, attempting to identify stable practices of conduct across different occasions and settings, with the goal of describing the underlying normative organization of interaction.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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".