Ejecting protestors, interpellating supporters: The interactional pragmatics of expulsion at Trump’s campaign rallies
Bibliographic record
Abstract
Abstract During his campaign for president in 2016, Donald Trump repeatedly instructed his supporters and event security to remove protesters from his rallies, most often, by issuing a directive to “get them out”. These occasions, far from being a distraction from the political process, emerged as potent rituals of participation and the activity of removing protestors became a tool of interactional messaging. Specifically, activities of ejecting protestors were semiotically and discursively elaborated so as to cast them as the virtual realizations of a larger political project of “making America great again.” Various aspects of this include the way these events came to signify about Trump’s persona and the brand of leadership he promised, about immigration reform and border control, about the possibilities for political participation and about a more diffuse struggle against the supposed tyranny of political correctness. Moreover, supporters who responded to the the instruction by attempting to remove protestors were interpellated by it as agents in the local scene of action and were thereby written into the larger populist narrative that Trump articulated.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.039 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| 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".