Bibliographic record
Abstract
This study looks at two figurative ways in which popular media and social media represent the publics response to the process of implementing Brexit. Specifically, it contrasts analogies, which construe the nature of Brexit in terms of the nature of the problems arising (e.g. the impossibility of taking the eggs out of the cake ), with tweets relying on simile to express emotional responses. The focus of this study is on the nature of simile, as the trope of choice in profiling emotional responses, and especially on narrativised similative constructions, such as Brexit is like X , where X as an extended narrative. These similes match the real story of Brexit, which lasted several years, with other narrative scenarios. Crucially, the scenarios created are focused on how the person feels about the story of Brexit (e.g. the long period of hesitation and indecisiveness) and not on political affiliations and arguments. In effect, Brexit is like X framing could be loosely paraphrased as Experiencing Brexit makes me feel similarly to experiencing a narrative such as X , where X is a made-up story, depicting unimportant social events or movie genres. The emotions targeted in the Brexit is like X examples (such as disappointment, boredom, feeling exasperated or bemused) are complex emotional reactions to a narrative failing to reach a satisfying resolution. From the perspective of figuration, Brexit is like X similes suggest the need to re-evaluate the nature of simile as a conceptual mapping and to consider the role fictive stories play in expression of emotions. Also, the complex syntactic forms used to represent the narrative structure of X provide the material for reconsidering simile as a construction.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".