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Record W6913057083 · doi:10.5683/sp/iognnn

Reconnecting Government with Youth 2004

2017· dataset· en· W6913057083 on OpenAlexaboutno aff

Bibliographic record

VenueBorealis · 2017
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Sample (material)DocumentationStakeholderCensus

Abstract

fetched live from OpenAlex

Reconnecting Government With Youth examines the connection between young people in Canada and the federal government. The 2004 study examines examines issues affecting youth today; youth crime, society, and remediation; government priorities; youth expectations of government; the internet; the job market; and aboriginal issues. The 2004 study's dataset consists of an initial sample of 2003 Canadians from across the country, aged 12-30, as well as a booster sample of 500 participants based on thier minority language status within their region of Canada. Those under the age of 18 were sourced through their parents, who are panelists in the Ipsos Consumer Panel. The sample has been weighted and is representative of Canada’s age and gender composition in accordance with census data. Several questions in this study have been tracked from previous Reconnecting Government with Youth Studies and are referenced as such in the accompanying reports. Some questions within the 2003 study were asked only to respondents in the 16-30 age range due to the difficult nature of the subject. Accompanying this dataset are questionnaire documentation including draft questions from stakeholder government departments, summary (topline) and comprehensive statistics from the data, as well as final reports and presentations.

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)0.001
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: Dataset
Teacher disagreement score0.933
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.011
Science and technology studies0.0030.000
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.010

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 head0.033
GPT teacher head0.274
Teacher spread0.242 · 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
Published2017
Admission routes1
Has abstractyes

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