Bibliographic record
Abstract
When Samara was created in 2009, we had little idea that the organization would be a part of an ambitious collaboration among academics and UBC Press that has brought this book to fruition.But it is from small seeds that big ideas grow -fitting in that Samara is named for the winged "helicopter" seed that falls from maple trees.Samara's research and educational programming began with the initiation of Canada's first-ever series of exit interviews with nearly eighty former members of Parliament.As citizens elected to represent and serve Canadians, they offered a wealth of information and frontline political experience that had been untapped previously.Our findings from this project, shared through four public reports released by Samara, have animated a broad public discussion on the role of MPs, their relationships to political parties, and how to improve Parliament.A 2014 Random House book, Tragedy in the Commons: Former Members of Parliament Speak Out about Canada's Failing Democracy, continues to bring forward the voices and experiences of MPs.Through the Samara Democracy Reports series, Samara's research agenda has expanded beyond the MP exit interviews while continuing to shine new light on Canada's democratic system.With the Samara 2012 Citizens' Survey, we regularly capture Canadians' shifting perceptions of politics and monitor civic and political participation.We have also analyzed the content
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.450 | 0.294 |
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".