Riding the Orange Wave: Leadership, Values, Issues, and the 2011 Canadian Election
Bibliographic record
Abstract
Abstract.The results of the 2011 Canadian federal election were surprising. What accounts for the dramatic and largely unexpected shift in electoral fortunes? Most importantly, what accounts for the sudden leap in popularity of the New Democratic Party, particularly in Quebec? The aftermath of election day produced no lack of potential explanations. Pundits, politicians, and political scientists have suggested many. This paper examines the empirical validity of various explanations swirling about the 2011 election, especially regarding the “orange surge.” The analysis relies upon the 2011 Canadian Election Study and the content of news media coverage. It concludes that the most important factors behind the orange wave were the image gap between Jack Layton and the other party leaders, as well as the proximity between the NDP's values and issue positions and those of many Quebeckers. Résumé.Le résultat de l'élection fédérale canadienne de 2011 a été surprenant. Comment expliquer les renversements dramatiques et largement inattendus dans les appuis aux partis politiques? Surtout, comment expliquer le bond soudain de popularité du Nouveau parti démocratique, particulièrement au Québec? Plusieurs explications potentielles ont été suggérées par les commentateurs, les politiciens et les politologues au lendemain de l'élection. Cet article examine la validité empirique de nombreuses explications entourant l'élection de 2011, notamment celles portant sur la « vague orange ». L'analyse repose sur l'Étude électorale canadienne de 2011 ainsi que sur le contenu de la couverture médiatique. Elle indique que les facteurs les plus importants à l'origine de la vague orange ont été l'écart entre l'image de Jack Layton et celle des autres chefs de partis, de même que la proximité entre le NPD et plusieurs Québécois quant aux valeurs et aux enjeux.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".