Introduction: Heteroglossia, performance, power, and participation
Bibliographic record
Abstract
In this special issue, we build on Bauman's seminal observation about performance, that ‘the act of expression is put on display, objectified, marked out to a degree from its discursive surrounding and opened up to interpretive scrutiny and evaluation by an audience’ (2000:1). More recently, scholars have moved to examining the performative role of heteroglossia, that is, the use of multiply sourced, semiotic (verbal and nonverbal) forms. In particular, this line of research has shown how attention to heteroglossic performances and their local interpretations can illuminate the subtle politics of dominant and nondominant identities in different ethnographic contexts. This is particularly true of what Coupland (2007) calls ‘high performances’, which, as Bell & Gibson (2011:558) write, are privileged sites for allowing participants to indexically associate expressive forms with social personae. Thus, while all performances are inherently reflexive, heteroglossic performances particularly amplify that reflexivity with respect to their multiple frames, voices, and stances that they presuppose and establish.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".