Bibliographic record
Abstract
†ood afternoon, my name is Adrienne Batra.I am the provincial director of the Canadian Taxpayers Federation.We are Canada's leading taxpayer advocacy group.We are trying to lower your taxes.We try to hold government accountable, and we also are trying to improve Canada's democracy by recommending change to government.It's not our job just to complain, it is also our job to give some options and give some change to how our public policy process can move forward in our country.I want to thank the organisers from the Western Frontier International Group for having me here today.I think, more often than not, we hear too much lip service paid to things like democratic reform, public policy, administration and accountability, and not enough is done to talk about it.So I thank you for having
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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 | 0.029 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.022 |
| Scholarly communication | 0.025 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".