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Record W7098593441

NEWS FROM HOME: M E MEDIA NEEDS OF CANADIANS IN THE U.S.A.

2016· article· en· W7098593441 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Taxonomy and Phylogenetics
Canadian institutionsnot available
Fundersnot available
KeywordsNaturalizationTourismPopulationService (business)News media
DOInot available

Abstract

fetched live from OpenAlex

Le nombre de citoyena canadiens voyageant e t s t instal lant aux Etats-Unis, ddpasse tous l e s re-cords. Cependant leurs habitudes a 1tQgard des media sont peu connues, notamment leurs besoins e t leurs sources de nouvelles enprovenance de leur patr ie. Cette enquete nationale a dtabli que l e e canadiens vivant aux Etata-Unis eprouvent 1 ' envie e t le besoin de trouver des informations canadien-nes dans l a presee amdricaine. Canadian citizens are relocating in large numbers in the United States. By the end of 1979, nearly 300,000 Canadians were listed under the a 1 ien reg istry program of the U S. Imnigration and Naturalization Service (INS). The nunber has been increasing at a rate of about 15,000 per year (INS, 1980, 83). Vacation travel by Canadians to the U.S.A. is also on the rise. The Canadian Off ice of Tourism reports a steady increase in vacation travel to American locations, despite a recent economic recession. In 1980, nearly three-mil 1 ion Canadians went to the U.S.A. on holidays, most heading to southern and western states (Off ice of Tourism, 1981, Little is known about the members of the Canadian population in the United States-- particularly their desire for and sources of home news while south of the border. Such information would be useful to a nunber of organizations. In some markets, including Phoenix, Arizona and Palm Beach, Florida, Canadians reside in sufficient numbers to Constitute a valuable often unmeasured "bonus " audience for American broadcasters and cablecasters. They also represent an attractive sub-

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.173
Teacher spread0.151 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2016
Admission routes1
Has abstractyes

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