Bibliographic record
Abstract
Abstract This paper considers a range of so-called image macro Internet memes and describes them as emerging multimodal constructions relying as much on image as on text, and apportioning roles to images much like constructional slots, for instance to fill in a subject role in a subjectless clause, or even to provide the main clause content to a textually given when -clause. In addition to existing or partially altered linguistic constructions, many examples also rely on specific top text/bottom text division of labor, and crucially depend on frame metonymy, with limited formal means quickly cueing richly detailed frames (for instance by using iconic images). The popularity of memes, forming series and cycles of iterations and remixes, and their role in establishing and maintaining discourse communities seems to be driven by a need to express and reconstrue viewpoints, often starting from ideas, affects or stereotypes assumed to be intersubjectively shared with viewers, whose responses they solicit. This paper argues that a proper description of Internet memes of the type considered requires a construction grammar approach, complemented by an understanding of viewpoint dynamics in terms of a Discourse Viewpoint Space regulating the network of spaces and viewpoints.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".