Bibliographic record
Abstract
The 'MIP data monthly' data set contains imputed values for the proportion of Canadians (in subsets of Canadians) identifying 27 different issues as the most important each month from October 1965 to November 2009. The imputed values are for the average Canadian, the Progressive Conservative Party base, the Liberal Party base, and the Reform Party / Conservative Party base. The imputation procedure is described in: Pickup, Mark and Colin Whelan. 2014. "Differentiating the issue priorities of NDP supporters." In The NDP. Eds. David Laycock and Lynda Erikson. UBC Press. This data has also been used in: Pickup, Mark, and Sara B. Hobolt. 2015. “The Conditionality of the Trade-off between Government Responsiveness and Effectiveness: The Impact of Minority Status and Polls in the Canadian House of Commons.” Electoral Studies 40: 517-530. The 'legislative success Canada' data set includes measures of the legislative success, popularity and minority governments status of Canadian Federal governments from the beginning of the 24th Parliament to the end of the 40th. This includes 42 sessions of Parliament and spans the temporal period 1958 to 2008. A description of the data and its use can be found in: Pickup, Mark, and Sara B. Hobolt. 2015. “The Conditionality of the Trade-off between Government Responsiveness and Effectiveness: The Impact of Minority Status and Polls in the Canadian House of Commons.” Electoral Studies 40: 517-530.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.083 | 0.067 |
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".