“All the other kids with the pumped up kicks” - \nan investigation of school shooting jokes told by American teenagers on TikTok
Bibliographic record
Abstract
Ever since the events of the Columbine Massacre in 1999 ushered in a new era of school violence, school shootings have been an ongoing concern for parents, teachers, and students alike in America. The reach and psychological weight of events such as these can be difficult to track. However, a deeper examination of folkloric forms, such as jokes and humorous narratives shared amongst students, may be an untapped resource. Dark and sick humour that is created and sustained by the student population may reveal more about the current state of youth mental health and perceptions of safety in relation to school shootings. This thesis investigates a collection of humour told and spread on the social media app TikTok, with a focus on the jokes told by current students and recent graduates of the American high school system. \nThese jokes are generally characterized by themes of distrust in the school system (including questioning the effectiveness of lockdown drills and other procedures), the inherent “Americanness” of school shootings, and anxiety about future shootings. Taken collectively, this body of jokes indicates that school shootings, and specifically the threat of future shootings, may occupy a larger portion of students’ anxieties than present psychological reports indicate. Additionally, these jokes illustrate the humorous culture that American students have built around the topic, which may be of use to scholars studying other aspects of school shootings for which there is little present data.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".