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Record W6950337707 · doi:10.5683/sp3/il1umf

Federal Elections in Ontario (1867-1911): Voting and Census Data file

2015· dataset· en· W6950337707 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2015
Typedataset
Languageen
Field
Topic
Canadian institutionsWestern University
Fundersnot available
KeywordsCensusVotingGroup voting ticketEthnic groupRanked voting systemOfficerInstant-runoff votingGeneral election

Abstract

fetched live from OpenAlex

The Federal Elections in Ontario (1867-1911): Voting and Census Data file was created to enable testing and hypotheses dealing with the effect of religious and ethnic background on voting patterns in Canada. Since subconstituencies tend to remain relatively stable from election to election, the subconstituency was selected as the basic unit of analysis. Each record consists of election returns data and census figures describing the ethnic and religious background of the subconstituency population. Election results were correlated with figures drawn from the next decennial census. Reports of the Chief Electoral Officer (for each election in the Parliamentary Sessional Papers), served as the source of the returns data. Following Dr. Kerr's sudden death in 1976, the data were deposited in the Social Science Computing Laboratory (SSCL) for use by other researchers. The data have been cleaned, documented and otherwise prepared for dissemination by SSCL (now Social Sciences Technology S ervices).

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)0.006
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 score0.041
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.017
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0310.018

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 head0.084
GPT teacher head0.309
Teacher spread0.225 · 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
Published2015
Admission routes2
Has abstractyes

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