Handbook of Political Behavior. The Personalization of Politics
Bibliographic record
Abstract
In a trend that has been shared by all of the liberal democracies, politics has become increasingly personalized. It is now commonplace for governments to be named after their leader, rather than after the party that holds office, particularly if the party and its leader have won successive elections. This is a phenomenon which is often traced to the election of Margaret Thatcher in Britain in 1979 and Ronald Reagan in the United States in 1980, two strong, charismatic leaders whose profile within the electorate easily eclipsed that of their respective parties. However, it is often forgotten that the earliest postwar manifestation of a leader surpassing the popularity of his party was Pierre Trudeau’s election as Canadian prime minister in 1968, when newly enfranchised younger voters responded to the new prime minister ‘swinger ’ image by giving birth to ‘Trudeaumania’. Nor is the trend towards the personalization of politics restricted to presidential systems, its traditional institutional home. The popular focus on leaders is now commonplace across almost all of the major parliamentary systems, where parties once occupied centre stage. The focus on leaders within parliamentary systems has been so
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.022 |
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".