Internet memes as multimodal constructions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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 it