Bibliographic record
Abstract
A report of the Special Committee on Electoral Reform (ERRE) of the Canadian Parliament was released in December 2016. ERRE, plus local consultations organized by members of Parliament, heard hundreds of witnesses. Many were political scientists. ERRE was created eight months after the 2015 election in which the Liberal Party of Canada, which had been third in the polls when the election was called, unexpectedly won a majority government. It had committed itself to making this the last election under first-past-the-post, a commitment the new prime minister, Justin Trudeau, reiterated upon taking power. This revived an electoral reform movement that had sought—but ultimately failed—to bring about change in the years 2004–2009 in the five provinces where it was on the agenda. Political scientists played an important role in each of the efforts. This latest effort has now met the same fate. This article surveys developments in the earlier round, going on to the present effort. In its conclusion, it asks what the comparative literature on such efforts has to teach us about this experience, and, conversely, what this experience adds to our understanding when and if such efforts can succeed.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.053 | 0.018 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".