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Record W4212982371 · doi:10.24124/2009/bpgub1394

Key performance measures in the BC Sheriff Service

2009· dissertation· en· W4212982371 on OpenAlexaffabout
Chris Nickerson

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of Northern British ColumbiaUniversity of British Columbia
Fundersnot available
KeywordsAppealStatuteVariety (cybernetics)Key (lock)Service (business)Performance indicatorPublic administrationEconomic JusticeSupreme courtBusinessComputer securityPolitical scienceLawComputer scienceMarketing

Abstract

fetched live from OpenAlex

This paper provides a mixed-method analysis of existing performance indicators in the provision of court security across Canada. In addition, it will provide recommendations on the development and implementation of performance metrics to the British Columbia Sheriff Service (BCSS) in the provision of court security. BCSS is charged under provincial statute and regulation to provide security to all levels of the provincial justice system, including BC Court of Appeal, Supreme Court, and Provincial Court. This includes provision of security for all types of trials held in a wide variety of facilities in various locations across the province. The BCSS has several key performance indicators in place that are used by the organization to measure effectiveness in various business areas including financial, human resources, and vehicle utilization. There is not, however, established performance metrics currently used to monitor or report on the operational effectiveness in the provision of security services, a core area of business for BCSS.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.810

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.044
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0090.016
Science and technology studies0.0030.001
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.112
GPT teacher head0.487
Teacher spread0.375 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2009
Admission routes2
Has abstractyes

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