Look at Us, We Have Anxiety: Youth, Memes, and the Power of Online Cultural Politics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Childhood is often defined by social marginalization— by a denial of access to public space and voice, and the circumscription of what interests, issues, and discourses are open to young people. The internet, as a space of expression that has lowered barriers to entry and confounded attempts at social control, is one of the primary spaces in which 21st-century youth are able to resist adult efforts to regulate their agency and expression. However, it is not only the technological tools of the online world that help them to resist marginalization from public discourse in these spaces, but also its symbolic and cultural resources—shared ideas, practices, and vocabularies particular to online communities. As part of larger projects on teens’ and young adults’ use of online communities to engage with cultural politics, I have been investigating the use of memes—patterns of formulaic content that rise and fall in popularity in short periods of time—in social critique, ideological discourse, community building, and identity representation. This paper examines the impact of memes on cultural discourse and, indirectly, on institutional politics, exploring their potential as an empowering channel of expression as well as their ongoing use as a tool of manipulation by larger political forces. I argue that understanding the cultural power of memes and other aspects of online remix culture is vital to theorizing contemporary politics, to analyzing the experiences and identities of contemporary youth, and to preventing the worst eventualities the increasing significance of online cultural politics may enable.
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.
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 | 0.001 |
| 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 it