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Record W4362663358 · doi:10.33137/ic.v8i.40879

The Multicultural History Society of Ontario and its Resources for Ethnic Studies

2023· article· en· W4362663358 on OpenAlexvenueaboutno aff
Gabriele Scardellato

Bibliographic record

VenueItalian Canadiana · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismEthnic groupPolitical scienceSociologyAnthropologyPedagogy

Abstract

fetched live from OpenAlex

The present study will focus on the availability of research resources for ethnic studies through the creation and work of the Multicultural History Society of Ontario.This perspective is amply justified because the Society itself, and in particular its Research Resources Department for which the present author is immediately responsible, are indeed a major research resource for ethnic studies.The Multicultural History Society of Ontario was created in 1976 with a substantial one-time grant from what was then the Province of Ontario's WINTARIO funding programme.From this beginning, those responsible for the project, especially the founding Director and President, the late Robert F. Harney, sought to create a "well-catalogued archival and library collection of ethnocultural material" which would be open to both the general public and scholars.'The creation of this research facility was seen as "one of the first steps toward a province which recognized the variety of its historical records which help free people from ignorance of one another and dangerous dependence on stereotypes.""To this end the Society has pursued a general mandate to "preserve and record the province's immigrant and ethnic history."The foundation occurred in an atmosphere which was very well "primed" for the pursuit of multiculturalism as a national or provincial ideology.It followed, of course, from the work of the Royal Commission on Bilingualism and Bimice

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.846
Threshold uncertainty score0.998

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.092
GPT teacher head0.302
Teacher spread0.210 · 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 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
Published2023
Admission routes2
Has abstractyes

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