The Language of Memes:Patterns of meaning across image and text
Bibliographic record
Abstract
The first book-length analysis of internet memes from the linguistic perspective, this book develops linguistic tools suitable for description and understanding of contemporary ‘image-plus-text’ communication, proposing the ‘grammar of memes’ as an example of a new approach to multimodal genres. What this book shows is how memes achieve meaning as multimodal artifacts, how they are governed by rules of composition and interpretation which are specific to memes, and how such processes are driven by stance networks. The approach demonstrates the emergence of constructions in which images become structural components, while making language forms adjust to the emergence of multimodal rules. Through analysis of a number of meme types, the approach defines the specificity of the memetic genre, describing types, but also accounting for creative forms. Of interest to linguists, discourse analysts, and media scholars, the book opens new questions about the role and nature of ‘image-plus-text’ discourse in contemporary communication.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".