The Scanlan's Monthly Story (1970-1971): How One Magazine Infuriated a Bank, an Airline, Unions, Printing Companies, Customs Officials, Canadian Police, Vice President Agnew, and President Nixon in Ten Months
Bibliographic record
Abstract
audit of Scanlan's and its stockholders.If a magazine's achievements can be measured in part by whom and how many it infuriated in the shortest amount of time, then surely Scanlan's deserves to be honored.In the midst of such special attention, Scanlan's managed to print some of the most provocative muckraking journalism of its time.It tackled a bewildering array of topics: atrocities by U.S. soldiers in Vietnam, the murder of a member of the Black Panther Party, Mexico-U.S. marijuana smuggling, the role of CBS in a failed invasion of Haiti, Mark Twain, the environment, Charles Manson, Russian pornography, the Mafia, counterfeit credit cards, and domestic guerilla warfare.Scanlan's also published the first examples of Hunter S. Thompson's nowcelebrated "Gonzo journalism," and two years before anyone outside of Washington, D.C., had heard of Watergate, it called for President Nixon's impeachment.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.048 | 0.011 |
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".