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Record W4386330951 · doi:10.1515/9781772126693-012

10 Inclusion on Whose Grounds? Against Liberal Essentialisms and toward Radical Neighbourliness in Rural Anti-racism

2023· book-chapter· en· W4386330951 on OpenAlexaboutno aff
Phil Henderson

Bibliographic record

VenueUniversity of Alberta Press eBooks · 2023
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)RacismSociologyPolitical scienceGender studies

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.191
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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