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How Distinctive Are Canadian Research Methods?*

2006· article· fr· W2153356334 on OpenAlexaboutno aff
Jennifer Platt

Bibliographic record

VenueCanadian Review of Sociology/Revue canadienne de sociologie · 2006
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceEthnologySociologyArt

Abstract

fetched live from OpenAlex

l'auteure de cet article aborde la question de la spécificité des méthodologies de recherche canadiennes, en examinant d'abord les grands modeles de méthodologies utilisés dans les articles empiriques publiés dans les principales revues canadiennes à partir des années 1960, comment ils ont changé au cours des temps et comment ils ont différé chez les anglophones et les francophones du Canada. Elle examine aussi la question de l'influence américaine soulevée par le débat qui s'est tenu sur la canadianisation. Il en ressort qu'au cours des dernières années les différences dues à la spécificité des sexes qui traversent ces divisions nationales ont été les plus importantes. l'effet final est que le modèle canadien global a quelque chose en commun avec ceux enregistrés dans les autres pays, quoique, à un niveau plus circonstancié, il soit spécifiquement canadien ou québécois. Les raisons des similarités et des différences sont analysées. This paper addresses the question of the distinctiveness of Canadian research methods by looking first at the broad pattern of methods used in empirical articles published in leading Canadian journals from the 1960s, how these have changed over time, and how they have differed between Francophone and Anglophone Canada. Issues of U.S. influence raised by the earlier Canadianization debate are also addressed. It is found that, for the more recent period, gender differences that cut across these “national” divisions have been the more salient. The net effect is that the total Canadian pattern has something in common with that recorded for other countries, although at a more detailed level there are specifically Canadian or Québécois effects. Reasons for the similarities and differences are discussed.

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)0.132
metaresearch head score (Gemma)0.271
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score0.999

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1320.271
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0200.041
Science and technology studies0.0140.027
Scholarly communication0.0340.009
Open science0.0050.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.137
GPT teacher head0.376
Teacher spread0.238 · 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.

Study designQualitative
DomainMethods
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

Citations20
Published2006
Admission routes1
Has abstractyes

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