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Record W2942405395

The Influence of Progressive Education in the History of Community Organizations in Canada, 1900-1950

2018· article· en· W2942405395 on OpenAlexaboutno aff
Caitlin Scharf-Way

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Education Studies and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsProgressivismProgressive educationCommunity organizationSociologyProgressive eraGirlPolitical scienceEarly childhood educationPedagogyPublic relationsSocial sciencePsychologyLawPoliticsDevelopmental psychology
DOInot available

Abstract

fetched live from OpenAlex

Herbart M. Kliebard states, in his view of progressivism in America, that there was no “unified,” progressive education movement. According to him, progressive education involved four interest groups- humanists (traditional views of learning), developmentalists (natural child development), social efficiency educators (effective, industrial views of school and society), and social meliorists (promoters of social justice and change)- that each had their own interests and agendas for education. This study will examine the tenets of these groups and extend them to the creation of community organizations  in Canada. The research questions include: What is the influence of progressive thought on the formation of child and youth community organizations in Canada? How were organizations like the Girl Guides, the 4-H Clubs, and the Boys and Girls Clubs of Canada implemented in response to progressive thoughts around children and learning from 1900-1950? What can we learn from this study about the connections between education and community in Canada’s past? I will use Kliebard’s interest groups as a framework in which to examine primary sources related to the creation of community organizations in Canada, including what is known today as the Girl Guides, 4-H Clubs, and the Boys and Girls Clubs of Canada.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.026
GPT teacher head0.297
Teacher spread0.271 · 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 designQualitative
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
Published2018
Admission routes1
Has abstractyes

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