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Record W1576460943

Decolonizing Education: Nourishing the Learning Spirit

2014· article· en· W1576460943 on OpenAlexvenueaboutno aff
Angela Mashford‐Pringle

Bibliographic record

VenueCanadian journal of native studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsMainstreamIndigenousSpiritualityCurriculumSociologyDecolonizationPedagogyAsidePublishingAestheticsLawPolitical sciencePoliticsLiterature
DOInot available

Abstract

fetched live from OpenAlex

Marie Battiste, Decolonizing Education: Nourishing Learning Spirit. Saskatoon, SK: Purich Publishing, 2013. 224 pages. ISBN 978-1-895-83077-4. $35 paperback.Marie Battiste builds upon her previous work in Aboriginal education with Decolonizing Education: Nourishing Learning Spirit. The book points to urgent need to decolonize education in Canada, especially for Aboriginals, who are not doing as well as non-Aboriginal students in mainstream curriculum. Intertwining personal stories with research findings, Battiste takes reader through issues of pedagogy and curriculum as they are today in Canada.The personal stories provide insights into Battiste's passion for Aboriginal education and creation of a environment that will decolonize and restore Aboriginal cultures, languages and ways of learning. Battiste argues that love of learning, or what she calls learning spirit, must be nurtured in order for Aboriginal students to succeed in mainstream education system. This spirit she describes as the entity within each of us that guides our search for purpose and vision (18). She argues particularly need for a spiritual connection to quest for knowledge, in which practice appropriate background and time must be set aside by educators to allow all students, especially Aboriginal students, to include their own spirituality in their journeys. Everyone must challenge existing system in order to design meaningful and honourable education for Aboriginal people...instead of biased fragmented concepts of culture buried in Eurocentric discourses (30).Indigenous knowledges and languages are unique and diverse. Battiste argues that there is a need to respect all Indigenous knowledge systems, of which language is a system unto itself that is too often neglected. She devotes a chapter to language and immersion programs because such initiatives, she suggests, will not only help Aboriginal students to connect to curriculum, but will ensure that future generations do not lose knowledge that is deeply embedded in all Aboriginal languages. Battiste provides ways to plan for and evaluate success of Aboriginal language revitalization programs.To achieve an Indigenous renaissance in education, Battiste argues that non-Indigenous allies in a wide variety of fields are vital to ensure that reclamation is true and on an equal footing to non-Indigenous knowledge systems. Yet to reach this first goal, Battiste notes that Indigenous scholars must begin to build bridges and make connections in order for non-Indigenous allies to work in collaboration for Aboriginal education and knowledge systems. This work should happen at various levels and within a variety of institutional systems in order for Indigenous methods to be accepted and applied. Although Indigenous methodologies are currently thought of as alternative to Western methodologies, in order to gain allies, decolonize education, and revitalize spirituality and language systems, Battiste argues that it is necessary for everyone involved to understand Indigenous methodologies and pass them on to emerging researchers, educators and curriculum developers in mainstream. However, because there is no one-size-fits-all Indigenous worldview, she argues it will be necessary to make particular curriculum changes by region as well as generally across board. …

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.020
Scholarly communication0.0100.009
Open science0.0010.009
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0060.002

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 head0.086
GPT teacher head0.417
Teacher spread0.331 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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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