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Record W4406938312 · doi:10.3138/cjpe-2024-0035

Planting the Seed: Learning From Co-Constructing Program Theory Within an Urban Indigenous Context

2024· article· en· W4406938312 on OpenAlexaffvenueabout
Laura Peach, Kelly Skinner, Hannah Tait Neufeld

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSowingIndigenousContext (archaeology)AgroforestryMathematics educationGeographyPsychologyEnvironmental scienceAgronomyBiologyEcologyArchaeology

Abstract

fetched live from OpenAlex

This evaluation work is grounded in the Northern Door: Be on a Good Journey, where the authors share their process for cultivating a place-based program theory for urban Indigenous land-based initiatives in the Waterloo Region, Ontario. This process was an essential first step to an ongoing evaluation project assessing the implementation and early outcomes of two programs for Indigenous children and youth. The authors embarked on this journey recognizing the need for relevant, place-based understandings of context that honour the philosophical and theoretical differences of Indigenous communities compared to Western knowledge. Drawing on guidance in both evaluation theory and Indigenous scholarship, four iterative sharing circles with key Indigenous community members were held to discuss the following thematic topics: program aims, activities, assessment needs, and initial theory assembly. Little published literature that describes culturally responsive program theory development is available. Necessarily, the authors are using this opportunity to develop, implement, and reflect on an urban Indigenous theory-development process.

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)0.011
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.063
GPT teacher head0.377
Teacher spread0.314 · 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
Published2024
Admission routes3
Has abstractyes

Explore more

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