Bibliographic record
Abstract
In "Satirizing Ethics," I explored three late-night satirical monologues from Full Frontal with Samantha Bee, The Daily Show with Trevor Noah and The Nightly Show with Larry Wilmore following the Orlando Nightclub Shooting on 12 June 2016.I examined the monologues using a social constructionist approach in order to understand what lessons each host believed could be drawn from the event and how a satirist references the nomos when tackling an issue in the world.Satire in this sense uses comedic tools like analogy, exaggeration, irony and sarcasm to point out follies within society and each host used his or her platform on late-night television to address issues they believed needed to be addressed following the shooting.I conducted a narrative analysis on each monologue in order to understand what the host and their team of writers thought about the event and what they thought should be the next step following another mass shooting in America.What I found in this sociological analysis of the monologues was that each host aimed to set an agenda in their monologues by emphasizing a) certain facts and information about the event and by b) presenting the audience with a way of viewing what happened and why the issue of gun violence and hate crimes in American needs to be addressed to ensure incidents like the one that happened in Orlando do not happened again.In other words, satire is the start of politics, as the host and his or her team of writers look to start difficult conversations with their audience about the world they live in and how it might improve through democratic means.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.069 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".