Bibliographic record
Abstract
On the surface, there was nothing special about the 2007 Newfoundland and Labrador general election, which saw Danny Williams re-elected for a second term as Premier. That his Progressive Conservatives would win a solid majority was never in doubt. There were no emerging issues, major gaffes or innovative campaign tactics, and few tight races. The de facto referendum on Williams’ leadership became a coronation. As Mackinnon (2007: 1) wrote about the Prince Edward Island election held five months earlier, “some campaigns are over before they begin.” In this case the only intrigue was how many Liberal or New Democratic Party candidates would form the opposition. However the results do illustrate that a relatively homogenous electorate can rally around a leader who decries the province’s status in the Canadian federation. Furthermore, when elected officials from all major parties have been implicated in a scandal, many electors respond by not participating in politics. Political scientists can therefore draw comparative insights, such as asymmetrical federalism reminiscent of Quebec Premier Jean Lesage in the early 1960s, political scandal similar to the Grant Devine administration of Saskatchewan in the early 1990s, or about civic engagement generally.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".