Bibliographic record
Abstract
This chapter considers the use of pronouns, and how they relate to such roles as Meme Maker, Meme Character (depicted in a meme’s image), and Meme Viewer (i.e. the ‘reader’ of a given meme). One illustration of how odd pronouns actually behave in memes is to consider the use of I , which does not refer to the Meme Maker, but is used to represent embedded discourses attributed to a depicted Meme Character. Just as curious is the use of me , in patterns such as Me Verb -ing , or Me/Also Me , which apparently instruct us to look for Meme Maker in the meme’s image, which in fact shows an unrelated Meme Character (possibly non-human, like an animal), such that the depicted character represents the experience of the Meme Maker. Such examples show that deixis is used in unusual ways in memetic discourse, to support the expression of viewpoint and stance targeted in the meme, rather than to identify specific referents.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".