For Better or Worse: How Political Consultants are Changing Elections in the United States
Bibliographic record
Abstract
For Better or Worse: How Political Consultants are Changing Elections in the United States, David Dulio, Albany: State University of New York Press, 2004, pp. xvii, 289. During the 2004 federal election, the media shone light on the political consultants who were reportedly affiliated or somehow related to Paul Martin's election campaign. By their account, the traditional party machine, often viewed to be the primary, if not the only, actor in political campaigns in Canada, seemed to have taken a backseat to the expensive, polished and professional campaign machinery the private sector had to offer. Campaign management through consultancy was now publicly visible in Canada and reliance on the party machine, while still important, seemed to face competition in terms of expertise and proximity to power. However, the study of political campaigns and specifically, the role of political consultants within campaigns, has received sparse attention from the political science community outside of the United States. Yet even in the US, in spite of the prevalent and pervasive presence of political consultants in electoral politics, the study of this group is relatively new.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".