Bibliographic record
Abstract
This appendix describes how I sampled and coded reports of media dissent and party discipline employed in Chapters 5 and 8. Sample selection I tracked media dissent and party discipline through several major dailies in Canada, Australia, and New Zealand, trying to balance the needs for regional coverage and quality reporting. Reports of media dissent and party discipline in Australia were tracked in three sources: (1) Sydney Morning Herald – the major source; (2) The Age (Melbourne) – for Victorian MPs and Senators; (3) West Australian (for West Australian MPs and Senators). MPs' names were used as search terms in each database. Newspaper stories appearing between 1 May 1996 and 30 September 1998 were coded for dissent and duplicates weeded out. Canadian media dissent and party discipline reports were tracked in the following sources: (1) Southam Newspapers ( Vancouver Sun, Calgary Herald, Winnipeg Free Press, Toronto Star, Ottawa Citizen, Montreal Gazette , and Halifax Daily News ); (2) Globe and Mail ; (3) La Presse . As in the Australian case, MPs' names were used as search terms in the above databases. The Canadian sampling frame dated from 5 November 1993 to 30 April 1997. The Canadian media databases were more flexible than the Australian databases (primarily because Southamowns newspapers in a number of urban centres) and permitted more geographically sensitive searches. I could, for example, search for a Quebec MP's name in the Montreal Gazette or La Presse instead of relying solely on the Globe and Mail (essentially the Canadian counterpart to the Sydney Morning Herald ).
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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 | 0.020 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.021 |
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".