Bibliographic record
Abstract
New media forms affect a culture, in part, by reshaping what is seeable and sayable: what “ideas,” as Neil Postman once put it, “we can conveniently express.” In this essay, I ask what one of today’s major new media forms—viral, digital “content”—compels us to see and say. To address that question, I em-brace a makeshift, hybrid methodology, informed by theory, sociology, arts criticism, and the digital humanities, and eschewing media theoretical orthodoxies that have been dominant across the humanities (namely: an exaggerated emphasis on the “medium” at the expense of the “message”). From this poly-glot perspective, I analyze content contained in a database that I have compiled, indexing 205,147 of the most-shared pieces of viral media on sites like Facebook and Twitter, from 2014 to 2019. After survey-ing this content’s basic features, I focus on one, particularly popular and quintessential content genre, which I call the “uplifting anecdote”: a short, sentimental account of a heroic act. The uplifting anec-dote, I argue, promotes a novel type of ethics, ideally suited to the content economy. I then track this ethics’ dissemination into the broader culture, through a discussion of two prominent, aesthetic artifacts: George Saunders’ prize-winning, best-selling novel, Lincoln in the Bardo (2017), and NBC’s popular sitcom, The Good Place (2016-2020).
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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".