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Record W4234946968 · doi:10.3138/cjpe.30.3.04

Lessons on Decolonizing Evaluation from Kaupapa Māori Evaluation

2016· article· en· W4234946968 on OpenAlexvenueno aff
Fiona Cram

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsAotearoaIndigenousSociologyTraditional knowledgeEquity (law)Christian ministryCommunity developmentCultural competencePedagogyPublic relationsPolitical scienceGender studies

Abstract

fetched live from OpenAlex

Abstract: Kaupapa Māori is literally a Māori way. It is a reclaiming by Māori (Indigenous peoples of Aotearoa New Zealand) of a future that is founded within a Māori worldview; a future where cultural knowledge and values inform understandings of and responses to Māori needs, priorities, and aspirations. Self-determination, cultural aspirations, and the importance of familial relationships and collectivity are among the central elements evident in Kaupapa Māori development initiatives. The culturally responsive evaluation of these initiatives builds upon traditional commitments to information management and the updating of Māori knowledge. Kaupapa Māori evaluation looks “inwards” to assess development on Māori terms, and “outwards” in a structural analysis of other facilitators of and barriers to that development. After more than 20 years of Kaupapa Māori evaluation, it is timely to ask what learning might helpfully be shared with other Indigenous peoples to support their desire for the culturally responsive evaluation of development initiatives they experience. A Kaupapa Māori evaluation lens will be described and then used to critique international development evaluation to facilitate decolonization. Audiences for this article include development efforts led by the New Zealand Ministry of Foreign Affairs within the South Pacific, and international development efforts led by organizations such as UNESCO that are developing equity evaluation approaches.

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)0.231
metaresearch head score (Gemma)0.204
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.769
Threshold uncertainty score0.948

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2310.204
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0160.019
Scholarly communication0.0210.017
Open science0.0050.018
Research integrity0.0050.014
Insufficient payload (model declined to judge)0.0070.001

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 head0.622
GPT teacher head0.597
Teacher spread0.025 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
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

Citations26
Published2016
Admission routes1
Has abstractyes

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