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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

<p> 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. </p> <p> Pour avoir accès aux plus récents tableaux de l'<strong>ESV</strong> veuillez vous diriger vers <strong><a href="http://www5.statcan.gc.ca/cansim/a33?lang=fra&spMode=tables&themeID=455&RT=TABLE" target="_blank"> CANSIM<a/></strong> </p> <p> Les données sont accessibles <strong><a href="http://odesi2.scholarsportal.info/documentation/JUSTICE/CJ/vss-esv/ESV.html" target="_blank">ici</a></strong> </p>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0040.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
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 teacher head, not a consensus.

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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