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Record W6969570370 · doi:10.5683/sp2/9x052k

Canadian Gallup Poll, December 1983, #480_1

2019· dataset· en· W6969570370 on OpenAlexaboutno aff

Bibliographic record

VenueBorealis · 2019
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsGeorge (robot)Prime ministerPublic opinionGovernment (linguistics)Control (management)Reading (process)

Abstract

fetched live from OpenAlex

This Gallup poll seeks the opinions of Canadians, on both political and social issues. The questions ask opinions about political leaders and political issues within the country. There are also questions on other topics of interest and importance to the country and government, such as the economy, media violence and plans for retirement. The respondents were also asked questions so that they could be grouped according to geographical variables. Topics of interest include: allowing cruise missile testing; the approval of Broadbent as NDP leader; the approval of Mulroney as leader of the opposition; the approval of Trudeau as Prime Minister; changes in the economy; changes in household finances; controlling the exposure to media violence; countries Trudeau visited during his peace talks; countries with freedom; the Federal government's influence on everyday life; the future of the economy; the impact of television violence on children; the impact of video game violence on children; making drivers take regular physicals; movie/television versions of George Orwell's 1984; opinion of Trudeau after peace talks; opinions about Canada's population; rating mail service; reading 1984 by George Orwell; retirement age; retirement plans; and ways to lose personal freedom. Basic demographic 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)0.013
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 score0.069
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0100.028
Science and technology studies0.0040.001
Scholarly communication0.0040.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0690.046

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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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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