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Record W6950497708 · doi:10.5683/sp2/2f65du

Canadian Gallup Poll, July 1969, #336

2019· dataset· en· W6950497708 on OpenAlexaboutno aff

Bibliographic record

VenueBorealis · 2019
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicGeological Studies and Exploration
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsGovernment (linguistics)Profit (economics)Basic incomeGeneral interest

Abstract

fetched live from OpenAlex

This Gallup poll seeks the opinions of Canadians on political and social issues of interest to the country and government. There are questions about elections, world conflicts, money matters and prices. The respondents were also asked questions so that they could be grouped according to geographical and social variables. Topics of interest include: the cutback of NATO forces in Europe; the dispute between Arabs and Jews; the amount of government money spent on Expo '67; opinions on who gets the most profit with the increased prices of vegetables; the amount of objectionable material in the media; the opinions about John Robarts; the opinions about topless waitresses; political preferences; provinces with power; the ratings of Stanfield as leader of the opposition; whether or not some proportion of income is saved; sex education in schools, the use of alcohol; which household member decides on money matters; which family member gets a fixed amount of pocket money; and who gets profit from the increased price of meat. 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)0.001
metaresearch head score (Gemma)0.010
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 score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0090.023
Science and technology studies0.0040.001
Scholarly communication0.0050.001
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0690.056

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 head0.026
GPT teacher head0.212
Teacher spread0.186 · 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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