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Record W4310438961 · doi:10.4324/9781003132691-33

Case Study

2022· book-chapter· en· W4310438961 on OpenAlexaboutno aff
Rachel Hale, Alistair Harkness

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldHealth Professions
TopicRomani and Gypsy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

North Battleford in west-central Saskatchewan in Canada, and Roma in Queensland in Australia, are rural communities which share several similarities. Canadian and Australian rural crime rates are often higher than urban crime rates. In 2014, North Battleford created a community safety strategy to address ongoing crime issues, reduce victimisation and build local business and capacity. As part of this strategy, four municipal employees attended a SafeGrowth training in Saskatoon, Saskatchewan. SafeGrowth involves four stages. In North Battleford, participants conducted a local risk assessment and determined that residents felt unsafe in the downtown area, attributed to high levels of social and physical disorder and the lack of maintenance. This discouraged residents from using the downtown area, and that further contributed to fear of crime and safety concerns for individuals who worked downtown. The local homeless shelter was an area of significant concern for residents and business owners, as it appeared to contribute to physical disorder.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.283
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0980.002

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 head0.233
GPT teacher head0.467
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

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
Published2022
Admission routes1
Has abstractyes

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