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Record W6912927128 · doi:10.5683/sp3/gkgivr

General Social Survey, Cycle 28, 2014 [Canada]: Victimization, Main File

2016· dataset· en· W6912927128 on OpenAlexaboutno aff

Bibliographic record

VenueBorealis · 2016
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMicrodata (statistics)Data collectionGeneral Social SurveyData fileSurvey data collectionCriminal justice

Abstract

fetched live from OpenAlex

The two primary objectives of the General Social Survey (GSS) are: to gather data on social trends in order to monitor changes in the living conditions and well being of Canadians over time; and to provide information on specific social policy issues of current or emerging interest. This survey collects information on the nature and extent of criminal victimization in Canada. The main objective of the GSS on Canadians' Safety (Victimization) is to better understand how Canadians perceive crime and the justice system. It also allows collection of information on their experiences of victimization. The 2014 GSS was a survey of individuals and contains two analytical files (main analytical file and incident analytical file). The microdata files from the main survey in the provinces contain questionnaire responses and associated information from 33,127 respondents. Analytical files for the survey in the territories contain responses and information from 2,040 respondents. Cycle 28 is the sixth cycle of the GSS to collect data on victimization. Previous cycles were conducted in 1988, 1993, 1999, 2004, and 2009.

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)0.002
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.052
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.019
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0040.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0520.018

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 head0.012
GPT teacher head0.246
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; 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
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
Published2016
Admission routes1
Has abstractyes

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