Bibliographic record
Abstract
This paper considers memes through the lens of riddles and discusses the generative or creative aspect of the meme format as applied in the classroom. In a literary studies course on cultural narratives, ranging from canonical to bestselling fiction, we critically discussed the genre-specific potential of memes, which students were encouraged to explore both intellectually and experientially. In addition, we asked students to create memes in their assessment of the course. The results were highly ambivalent, ranging from humor to seriousness, self-critique to critique of the course, panic (regarding the final exam) to playful exaggeration of said panic. This ambivalence, often accentuated by irony and excess, challenges any definitive understanding of the memes’ content and meaning. Rather than dismissing memes as a flawed, imprecise tool, this article examines them as riddled forms and hypothesizes that, due to their ambivalence, they may actually be closer to a student’s “truth.” The connection between memes and meaning-making is especially relevant to courses that, like the one in this article, foreground semantic ambiguity and an explorative habitus.
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.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".