Sept 1, 2022 - an exorcism in Saskatchewan, a standoff, dueling brothers, and a stolen bike
Bibliographic record
Abstract
Jordan and his pal Aaron discuss, explore, and celebrate the week in offbeat Canadian news.In this episode Aaron and I catch up with the freedom convoy adjacent group that has been using a 100 year old church in Ottawa as their headquarters, we are nearly left speechless by an exorcism that recently took place at a Saskatchewan bible camp, and we share two interesting stories concerning current municipal elections.Links:Keep Canada Weird Series: https://www.nighttimepodcast.com/keep-canada-weirdJoin the Keep Canada Weird Discussion Group: https://www.facebook.com/groups/keepcanadaweirdSend a weird news tip: https://www.nighttimepodcast.com/contactProvide feedback and comments on the episode:nighttimepodcast.com/contactSubscribe to the show:premium feed:https://www.patreon.com/Nighttimepodcastapple podcasts: https://applepodcasts.com/nighttimeMusical Theme:Noir Toyko by Monty DattaContact:Website: https://www.nighttimepodcast.comTwitter: https://twitter.com/NightTimePodFacebook: https://www.facebook.com/NightTimePodInstagram: https://www.instagram.com/nighttimepodSupport the show: https://www.patreon.com/nighttimepodcastLearn more about your ad choices. Visit megaphone.fm/adchoicesLearn 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.137 | 0.024 |
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".