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Record W6894343508 · doi:10.5683/sp2/2oxz3p

Canadian Gallup Poll, November 1965, #315

2019· dataset· en· W6894343508 on OpenAlexaboutno aff

Bibliographic record

VenueBorealis · 2019
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsVotingPoliticsDemographicsChinaPublic opinionGovernment (linguistics)Survey data collection

Abstract

fetched live from OpenAlex

This Gallup poll aims to collect the opinions and views of Canadians on issues of importance to the country. The survey questions are predominantly politically based, asking about preferred leaders and parties, as well as about other issues important to the country and government. The respondents were also asked questions so that they could be grouped according to geographic, demographic, and social variables. Topics of interest include: Canada's relations with the United States; car ownership; causes of high prices; economic conditions; federal elections; French/English relations; which leader would be best for national unity; which political parties are best fo certain groups; whether Russia would side with China or the United States in a war; the success of political campaigns; union membership; voting behaviour; and whether women shold be given equal opportunity for jobs. Basic demographics variables are also included.

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)0.011
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 score0.058
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.022
Science and technology studies0.0040.001
Scholarly communication0.0050.001
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0580.050

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 head0.021
GPT teacher head0.266
Teacher spread0.245 · 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
Published2019
Admission routes1
Has abstractyes

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