The 2020 Provincial Election in New Brunswick: The First Canadian COVID-19 Election
Bibliographic record
Abstract
AbstractNew Brunswick’s 2020 election was Canada’s first election during the COVID-19 pandemic. It produced a slim majority government for the Progressive Conservatives under Premier Blaine Higgs after all-party talks to create a quasi-coalition arrangement failed. The major parties continued to decline in voter support, and two newer parties, the Green Party and the People’s Alliance, still have a presence in the Legislative Assembly. The Liberal Party failed to win seats in Anglophone New Brunswick, reducing their support to just the francophone areas of the province. Higgs is left to govern a province polarized along linguistic lines with French-speaking New Brunswickers distrustful and unsupportive of the premier.RésuméL’élection provinciale au Nouveau-Brunswick en 2020 a été la première élection provinciale au Canada pendant la pandémie du COVID-19. Elle a mené à l'élection d'un gouvernement avec une faible majorité pour le Parti progressiste-conservateur sous le premier ministre Blaine Higgs après l'échec des pourparlers entre tous les partis pour la création d'un arrangement de quasi-coalition. Les partis traditionnels continuent de voir leur soutien s'effriter au sein de l’électorat, et deux nouveaux partis, le Parti vert et l’Alliance des gens, ont toujours des représentantes à l’Assemblée législative. Le Parti libéral n'a pas réussi à remporter des sièges dans les régions anglophones de la province, réduisant son soutien aux seules régions francophones de la province. Les considérations linguistiques jouent un rôle de premier plan dans la polarisation politique dans la province. Higgs doit ainsi gouverner en tenant compte des citoyennes francophones qui sont méfiantes et peu favorables au premier ministre.Key words: New Brunswick, Election, Election campaigns, polls, Majority government, BilingualismMots-clés : Nouveau-Brunswick, Élections, Campagnes électorales, Sondages, Gouvernement majorité, Bilinguisme
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".