10 Inclusion on Whose Grounds? Against Liberal Essentialisms and toward Radical Neighbourliness in Rural Anti-racism
Bibliographic record
Abstract
Owen Sound, Ontario, experienced a rash of white supremacist vandalism.Racist stickers were affixed throughout public spaces in this small town in the predominantly rural federal riding of Bruce-Grey-Owen Sound.Lightposts, mailboxes, handrails, street signs, and park benches were remade into messaging boards for hatred, unified under white identitarianism (Finlay-Stewart 2019).Furthering this atmosphere of hate, on July 11 and 12, 2019, the Owen Sound Muslim Association-a place of communal worship-was also vandalized.Eggs, tomato sauce, and condiments were smeared onto the windows, walls, and roof.A chain restaurant operated by members of the Muslim community was similarly defaced.At a regularly scheduled city council meeting on July 15, Mayor Ian Boddy denounced these events, labelling them as hate crimes, and comparing them to a series of arsons that rocked the city in 2015.Echoing sentiments also expressed by Muslim Association spokesperson Waleed Aslam, Boddy argued that these actions were unreflective of Owen Sound (Langlois 2019).Boddy instead praised the roughly 70 people who attended a solidarity vigil at the Muslim Association on July 13, held in hopes of deterring a third night of vandalism.Whether because of their presence or some as-yet-unknown reason, no further vandalism Inclusion on Whose Grounds?Against Liberal Essentialisms and toward Radical Neighbourliness in Rural Anti-racism Phil Henderson 10
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.041 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".