Bibliographic record
Abstract
Could changing Canada's electoral system solve the crisis of voter apathy? Today, we dive into electoral reform. Plus, a new bill that would give border guards wide discretion to search our phones and computers.UPDATE: After we recorded on Monday, senators on the national security and defence committee passed an amendment to Bill S-7 to replace the term \\"reasonable general concern\\" with \\"reasonable grounds to suspect.\\" Read more here: https://nationalpost.com/news/politics/senate-amends-bill-that-would-create-controversial-new-standard-for-cellphone-searches-at-borderPanelists: Drew Brown, David Moscrop, Raisa PatelLinks: Read Senator Paula Simons' op-ed about Bill S-7 in The Line: https://theline.substack.com/p/paula-simons-the-government-is-tryingSponsors: Douglas, 22 Murders, The Peak See acast.com/privacy for privacy and opt-out information.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.103 | 0.028 |
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".