Bibliographic record
Abstract
On 7 December 2010, Toronto’s newly elected mayor, Rob Ford, was sworn in at City Hall. In keeping with recent shifts towards the right in Canadian politics, Ford rode to victory on a populist Tea Party-style platform promising small government, tight spending and tax cuts. 1 Setting the tone for a new era of municipal politics — one that has placed a combative mayor at the centre of highly theatrical and seemingly endless public scandals — Ford invited controversial hockey commentator Don Cherry to attend the ceremony as his special guest and gave him the honour of hanging the chain of office around his neck. 2 Cherry, a celebrity known not only for his political conservatism but also for garish attire, showed up in a flamingo pink floral-print blazer, a costume designed to match his equally colourful remarks. ‘I’m wearing pinko for all the pinkos out there that ride bicycles and everything’, he declared, going on to slam the left-wing media who turn up their noses at his church attendance and patriotic support of the troops. ‘This is what you’ll be facing, Rob, with these left-wing pinkos — they scrape the bottom of the barrel’ (in Nurwisah 2010). A few days earlier in an interview about Ford’s win, Cherry gave this rationale for his upcoming appearance in council: ‘People are sick of the elites and artsy people running the show […]. It’s time for some lunch pail, blue-collar people’ (in Rider 2010). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.050 | 0.050 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".