Book Review: <em>Kings, Conquerors, Psychopaths: From Alexander to Hitler to the Corporation</em>
Bibliographic record
Abstract
The book Kings, Conquerors, Psychopaths is a survey of a vast amount of human wrongdoing. It lays bare the motivations of aggressors who wish to subjugate nations or groups of people and corporate executives and government bureaucrats who make discretionary decisions that harm people. Along with cataloging mass killings by despots and soldiers, the book includes stories about Ponzi-schemers and the deaths of automobile drivers and passengers who were killed by vehicle defects known to the manufacturer. The book posits that “[p]owerful, elite forces are trying to force us backward toward a non-democratic state, one where power, wealth, and prerogative are concentrated into fewer and fewer hands.” In its criticism of the judgment of corporate executives and government bureaucrats, the book could be a tract for intellectual populists. While the power of corporations and bureaucracies can be vast and unrelenting, and as dominating as kings, the book might have made clearer distinctions between direct, intentional killers, like the “psychopaths” and conquerors, and the bureaucrats who run corporations and government agencies. The vivid stories in the book are reminders that democracy and civic action are necessary in countering wrongdoers.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.100 | 0.063 |
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".