Bibliographic record
Abstract
Abstract The outcome of the 7 April 2014 general election in Quebec proved to be a surprise to many observers. Voters across Quebec chose to support a pro-federalist, Liberal Party majority government, led by Philippe Couillard. Pauline Marois’s overtly separatist Parti Québecois (PQ) was soundly and unexpectedly defeated. The 33-day electoral campaign, marked by a heightened focus on Quebec independence, identity politics and the proposed extension of further protections for the French language, illustrated that Quebec society was far more concerned with issues surrounding the scope and delivery of healthcare, education and a whole host of related economic issues, including employment, provincial debt levels, public expenditures, taxation and the pace of economic growth. This essay, in examining the election campaign, suggests that the preferred message of the PQ failed to resonate with Quebec public opinion; a message that was only further muddled with the introduction of ‘star’ candidate Pierre Karl Péladeau. The results of the 2014 election, this essay concludes, further points to significant shifts underway in Quebec society; shifts that portend important new currents in public attitudes and the very relationship between the province’s residents and the Quebec state.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".