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Record W6981576856

Errors in Canadian history culled from "Prize answers".

2014· article· en· W6981576856 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2014
Typearticle
Languageen
FieldMedicine
TopicMagnolia and Illicium research
Canadian institutionsnot available
Fundersnot available
KeywordsHeading (navigation)Chose
DOInot available

Abstract

fetched live from OpenAlex

pression made use of by Sagard (page 174), " crosser une balle de bois leger comme Von f aid en nos quartiers" it would appear that it was not unlike some game then played in France.But Hermes might seem to have discovered a still earlier reference to the game, for his answer, as to when it is first mention- ed, is " 1608.Le jeu de crosse.Ferland's History of Canada, vol.I, page 133."This was accepted by the Spectator as correct."Upon referring to the authority cited, I was astonished to find that page 133 forms part of a chapter on the Indians, their customs, etc., and that " 1608 " is only a portion of the running heading of that chapter, and by no means intended for the date of the first mention of any of the customs therein described.Besides, Ferland could have given such an early date only on authority other than his own, his history being a recent publication.Hermes may perhaps be able to account for this singular error.FIRST MILITARY ORGANIZATION ON RECORD.. Question No. 21 was : " What is the oldest mili- tary organization of which there exists an authentic record of formation," and Hermes states that it is the celebrated Carignan Regiment, which was disbanded in 1668, soon after its arrival in Canada.He says : "Tractsof land were granted to its officers and men who chose to settle in the Colony, and, in case of attack by the Iroquois or by the Anglo-American colonists, they were expected,

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)0.003
metaresearch head score (Gemma)0.020
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: Empirical · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.503

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.019
Science and technology studies0.0250.008
Scholarly communication0.0120.005
Open science0.0030.003
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0700.021

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 head0.010
GPT teacher head0.197
Teacher spread0.187 · 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
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
Published2014
Admission routes1
Has abstractyes

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