Canadian Election Survey, 2000
Bibliographic record
Abstract
This survey assessed Canadians' political attitudes and voting behavior prior to the 2000 federal election. The survey included three components: the Campaign-Period Survey (CPS), the Post-Election Survey (PES), and the Mail-Back Survey (MBS). Approximately 46 percent of the telephone numbers included in the CPS were completed for a total of 3,651 interviews. Seventy-eight percent, or 2,860 of the CPS respondents, completed the PES survey, and 1,517 of the PES respondents completed the MBS. The CPS respondents were queried on their voting intentions, interest in the election and its media coverage, whether parties/candidates had contacted them during the campaign, the state of the economy, knowledge of the parties and leaders, personal stances on major policy issues such as cutting taxes, maintaining social programs, and the possible separation of Quebec from Canada, assessment of the Liberal government, and electoral expectations. Specific questions on political actions and personal character were posed regarding Prime Minister Jean Chretien, Conservative Party Leader Jean Charest, New Democratic Party Leader Alexa McDonough, Reform Party Leader Preston Manning, Bloc Quebecois Leader Gilles Duceppe, Premier Lucien Bouchard, and former Prime Minister Brian Mulroney. The PES repeated many of the CPS questions, and addressed additional topics such as government spending, social issues including abortion, unions, businesses, education, health care, and capital punishment, Quebec separation, and attitudes toward social groups including big business, feminists, and aboriginal peoples. The MBS dealt with broader political issues and values, including the respondents' confidence in institutions, the distribution of power among various societal groups, and individual rights. Background information on respondents includes age, sex, ethnicity, political party, political orientation, voter participation history, education, marital status, religion, employment status, household income, union membership, country of birth, knowledge of Canadian political history, financial status, and disability status.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.020 | 0.043 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.039 |
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".