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Record W6950610959 · doi:10.5683/sp3/djsgwi

Enquête sur les services aux victimes [Canada] [B2020]

2018· dataset· fr· W6950610959 on OpenAlexaboutno aff

Bibliographic record

VenueBorealis · 2018
Typedataset
Languagefr
FieldComputer Science
TopicLogic, Reasoning, and Knowledge
Canadian institutionsnot available
Fundersnot available
KeywordsInformation centerService (business)Poison control

Abstract

fetched live from OpenAlex

Cette enquête agrégée a pour objectif de recueillir des renseignements sur les organismes de services aux victimes d'actes criminels qui ont offert des services directement aux victimes principales ou secondaires d'un acte criminel pendant la période de déclaration de 12 mois et de donner un aperçu instantané d'une journée des clients desservis un jour donné. L'enquête recueille aussi de l'information sur les activités des programmes d'indemnisation pour les victimes d'actes criminels et des autres programmes de prestations financières durant la période de déclaration de 12 mois. L'enquête a été élaborée en 2002 et en 2003 en raison de l'absence de renseignements sur les services pour les victimes d'actes criminels et les clients qui les utilisent. Avant l'élaboration de l'Enquête sur les services aux victimes d'actes criminels, la seule source de données nationales à ce sujet était l'Enquête sur les maisons d'hébergement de Statistique Canada, qui recueille des renseignements sur les services d'hébergement pour les femmes violentées et leurs enfants. Pour avoir accès aux plus récents tableaux de l'ESV veuillez vous diriger vers CANSIM Les données sont accessibles ici

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.060
Threshold uncertainty score0.422

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0080.001
Scholarly communication0.0050.001
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0600.010

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 head0.024
GPT teacher head0.244
Teacher spread0.220 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2018
Admission routes1
Has abstractyes

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