Bibliographic record
Abstract
This poll is the first wave of a 5-wave in-campaign poll of the 2022 Ontario General Election. The poll was conducted between March 11-15, 2022, and the election was held on June 2, 2022. The poll’s primary focus was the Ontario election and included election-related questions directed to respondents from Ontario. The poll also included a subset of questions focused on national concerns that were directed to respondents from across Canada. Respondents from outside Ontario did not participate in questions related to the election. In total, the poll sampled n=1500 Canadians aged 18 and over, including 850 Ontarians, via the online Ipsos I-Say Panel and non-panel sources. Respondents earned a nominal incentive for their participation. Respondents are geographically identified by province, census division, region of Canada, and region of Ontario. Respondents provided information regarding government and party preferences, voting intentions, party leadership, the federal budget, inflation, and the cost of living. This included questions on party preference, the premiership of Doug Ford, and the leadership potential of all provincial party leaders. Weighting was employed to ensure the sample’s composition reflected the adult population according to 2016 Census data. To maintain respondent anonymity, a number of variables were not collected or have been removed by the panel software and appear as missing within the dataset. This includes ethno-cultural identity variables and postal codes of respondents. This dataset includes microdata in SPSS .SAV format, a script, supplemental information on weighting factors, and aggregated statistical tables The statistical tables enable a simple analysis of the polling data based on variables such as sex, age, education, location, household income, and composition.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.014 |
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".