The Political Poetics of Contrastive Identifications: Scripting Ongoing Events Through Master/Counter Positioning in Populist Political Speech
Bibliographic record
Abstract
The article reports an empirical inquiry into the rhetorical use of master/counter juxtapositions in narrating an ongoing scene of political action. Drawing on the studies of institutional interaction, it investigates the institutional setting of public political speeches, focusing on the empirical example of Donald Trump’s public speech in the rally after the election results and just before the violent invasion of the United States Capitol on January 6, 2021. My research approaches master/counter positioning as a multilayered relational constellation of identifications mobilized in the telling and deployed strategically for specific institutional purposes. I am interested in counter-narratives as interpretative discursive frames superimposed on surrounding socio-material circumstances to refute an alternative (pre-existing and prevailing) interpretation of reality. My take on the concept as a multifaceted rhetorical resource subsumes the aspects of an act of contestation, a breach of cultural orders, and a mission towards emancipation, albeit in a slightly modified version of the conventionalized definitions. In the context of political interaction, that is, in institutional activities connected to ongoing processes in policy-making and governance, counter-narratives are world-breaking but they are also world-making in a decidedly concrete consequential manner, firstly, by building on institutional continuities, virtues, and legitimacies, and secondly, by addressing recipients as co-actors, projecting identifications on them and expecting them to assume a role in the political participation field at hand. Applying tools from small story research, membership categorization analysis, epistemic governance and narrative positioning analysis, I explore the purposeful evocation of contrastive storylines in political rhetoric. The article aims to shed light on the argumentative use of counter-narratives in a political line of action.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".