Expectation and anticipation: research assemblages for elections
Bibliographic record
Abstract
In this paper we interrogate how different research assemblages act as affect-enhancing devices for elections by drawing from the 2015 Canadian Federal election. The paper uses scholarship from media events traditions to devise an ontological framework for analysing the mediatization of elections and to show how research assemblages propagate a myth of the mediated centre. We then discuss two examples of how research assemblages are deployed as mood enhancing devices within election coverage. The first example focuses on how polling data is deployed to generate and sustain a myth of the mediated centre within an ontology of expectation. For the second example, we turn to how the emergence of a participatory condition in contemporary sociality introduces an ontology of anticipation that further problematizes the role of research assemblages in the mediatization of elections. In the final sections of the paper, we examine a case study of Creative Publics: Art-Making Inspired by the Federal Election to discuss alternative approaches to researching elections that also draw on an ontology of anticipation. We show how alternative research assemblages can channel the anticipation generated by participatory politics to yield more diverse and critical forms of participation in the lead up to elections.
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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 | 0.030 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.017 | 0.051 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".