Recap of Hamilton City Council this week!
Bibliographic record
Abstract
The Good Morning Hamilton Podcast w/ Scott Radley:To recap Hamilton City Council this week - from encampments to taxes to the city seeking major tourist events - John Best joins Scott. GUEST: John Best, Publisher, The Bay Observer. -A local Hamiltonian will be celebrating her 109th birthday this weekend! GUEST: Laura Konkel, granddaughter of Ann Konkel-Does tipping encourage better service? GUEST: Michael von Massow, OAC Chair in Food System Leadership, Associate Professor, Food Agriculture and Resource Economics, University of Guelph-Why does everyone hate Nickelback? The documentary about the band is now up on Netflix. ALSO: what is the song of the summer? GUEST: Eric Alper, Publicist, music commentator-A new report from Royal LePage says that last month's long-awaited rate cut by the Bank of Canada was not enough to drive sidelined Hamilton homebuyers into the market. GUEST: Mike Heddle (Broker and team Leader, Royal LePage State Realty)-Some PMO officials are worried that the economic messaging from Chrystia Freeland has become ineffective. GUEST: Genevieve Tellier (Pronounced: John-vee-ev Tell-yay), Professor, School of Political Studies, University of Ottawa. Learn more about your ad choices. Visit megaphone.fm/adchoices
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.716 | 0.432 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".