Bibliographic record
Abstract
Internet memes are an extremely popular form of online communication that are often disregarded as “trivial” due to their association with humor, emotion, and youth (Shifman, 2014, p. 15; Shifman, 2019). While these digital texts (e.g., image macros, tweets, hashtags, short videos, GIFs, etc.) play a significant role in the circulation of information (e.g., Milner, 2016), there is little empirical research on how they figure into young people’s daily information practices. To address this issue, I collaborated with a secondary school teacher, co-designing a class unit that invited students to examine the relationship between memes and digital citizenship. Adopting an ethnographic approach (Markham, 2017) inspired by several design-based research methodologies, I collected a variety of data from 21 youth participants (e.g., field notes/photos, assignments), performing 15 semi-structured student interviews and 2 semi-structured teacher interviews. These materials, which I analyzed using narrative and visual analysis (Frank, 2012; Rose, 2016), included student projects on various meme-related topics (e.g., free speech, dark humor, misinformation, etc.). My findings showcase the significance of humor to students’ conceptualization of memetic storytelling and the joyful nature of the personal experiences they associated with it. Laughter, I argue, impacted their negotiations of the values memes represent and shaped the information they provided. Through its conceptualization of the informational role humor plays in young people’s meme engagements, this research contributes to present knowledge of how memes factor into civic discourse, offering valuable insight to educators, librarians, and guardians who engage youth in information literacy and citizenship education.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".