Bibliographic record
Abstract
Abstract Almost forty years ago, Neil Postman argued that television had brought about a fundamental transformation to democracy. By turning entertainment into our supreme ideology, television had recreated public discourse in its image and converted democracy into show business. In Trolling Ourselves to Death, Jason Hannan builds on Postman’s classic thesis, arguing that we are now not so much amusing, as trolling ourselves to death. Yet, how do we explain this profound change? What are the primary drivers behind the deterioration of civic culture and the toxification of public discourse? Trolling Ourselves to Death moves beyond the familiar picture of trolling by recasting it in a broader historical light. Contrary to the popular view of the troll as an exclusively anonymous online prankster who hides behind a clever avatar and screen name, Hannan asserts that the trolls have emerged from the cave, so to speak, and now walk in the clear light of day. Trolls now include politicians, performers, patriots, and protesters. What was once a mysterious phenomenon limited to the darker corners of the Internet has since gone mainstream, eroding our public culture and changing the rules of democratic politics. Hannan shows how trolling is the logical outcome of a culture of possessive individualism, widespread alienation, mass distrust, and rampant paranoia. Synthesizing media ecology with historical materialism, he explores the disturbing rise of political unreason in the form of mass trolling and sheds light on the proliferation of disinformation, conspiracy theory, “cancel culture,” and digital violence. Taking inspiration from Robert Brandom’s innovative reading of Georg Wilhelm Friedrich Hegel, Trolling Ourselves to Death makes a case for building “a spirit of trust” to curb the epidemic of mass distrust that feeds the plague of political trolling.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".