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Record W1580302393

GROWTH OF MULTIDISCIPLINARY SCIENCE IN THE PERIPHERY

2014· article· en· W1580302393 on OpenAlexaffabout
Kevin O’Connor, Robert F. Sharp

Bibliographic record

VenueEuropean Scientific Journal ESJ · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsYukon Department of EducationMount Royal University
Fundersnot available
KeywordsIndigenousExperiential learningMultidisciplinary approachCurriculumCommunity engagementPlace-based educationPolitical scienceField (mathematics)Experiential educationRural areaPublic relationsGeographySociologyPedagogySocial scienceEnvironmental educationEcology
DOInot available

Abstract

fetched live from OpenAlex

This paper examines practice, growth and research associated with numerous experiential science, field studies and related place-based activities incorporated in a number Indigenous and rural communities in northern Canada. These approaches to science are presently being applied in the public school systems of the northern Canadian Territories (Yukon and Nunavut), and in numerous Cree and Dene Nation reserve schools in the northern Provinces of Alberta, Saskatchewan and Quebec. The research has followed students over a range of years following their engagement in these programs exploring their ongoing participation in the field of science and in community affairs. The capacity for communities to direct and manage their own educational programs has been a central concern particularly in rural, remote and Indigenous communities. Characteristics of peripheral communities provide opportunities to initiate and manage various conditions of social change. This paper identifies those characteristics that favor educational approaches that require changes in how schools organize time, teaching staff, and curriculum offerings. These include experiential and place-based science approaches. These approaches have shown a greater engagement and improved outcomes of Indigenous and rural students.

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)0.029
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
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 head0.022
GPT teacher head0.299
Teacher spread0.277 · 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.

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
Published2014
Admission routes2
Has abstractyes

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