Bibliographic record
Abstract
Frequently cited in the literature on recent Thai politics, Duncan McCargo's influential "network monarchy" concept is nonetheless inadequately developed. As such, it has been questioned and challenged by several scholars in recent years. In his 2021 Pacific Affairs article, McCargo rebuts many of these scholars' arguments and defends his concept. His defence is unpersuasive, however. It falls short of elaborating on the scope, composition, and modus operandi of network monarchy, leaving the shortcomings of his original concept unrectified. Most seriously, McCargo now accentuates the "ambiguous" quality of network monarchy—a quality he did not emphasize originally—in order to accommodate new empirical anomalies and counter his critics. By so doing, he renders his argument unfalsifiable. Drawing on Robert Cribb's thoughts, this article first spells out why or how the insu ciently developed network monarchy concept has become so widespread in the first place. It then examines the untenable nature of McCargo's rejoinder to his critics, especially to Eugénie Mérieau.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".