Bibliographic record
Abstract
This chapter turns to labelling memes, where some images may develop into full-blown Image Macros, while others remain non-entrenched. Here, the textual component is different from both when -memes and from the typical Image Macro memes. In typical labelling memes, parts of a depicted scene are labelled with words or phrases which do not describe anything in the image, but instead collectively call up a different frame. Well-known examples discussed include the Is This a Pigeon? meme, and the Distracted Boyfriend meme (DBM), showing a man turning over to admire an attractive passing woman (dressed in red), while the woman (in blue) whose hand he’s holding looks on indignantly. This scene of a change in attention and preference – a choice for a new and attractive opportunity – gets to be applied to unrelated choices and new preferences. Labelling itself can sometimes be visual again. Overall, we stress the constructional properties of DBM – with strong argument structure-like properties – alongside the role of embodied features (emotions and attentions expressed in facial expressions and posture) and the figurative, similative meaning often arrived at compositionally.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.016 |
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".