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Record W4409825297 · doi:10.1177/11771801251334772

Nurturing roots: a scoping review on Indigenous acts of resistance through Land-based healing practices

2025· review· en· W4409825297 on OpenAlexaff
Madison Cachagee, Brianna Poirier, Clarence Cachagee, Lisa Jamieson, Hannah Tait Neufeld

Bibliographic record

VenueAlterNative An International Journal of Indigenous Peoples · 2025
Typereview
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of WaterlooDeer Lodge Centre
Fundersnot available
KeywordsIndigenousResistance (ecology)Environmental planningPolitical scienceEnvironmental ethicsSociologyGeographyBiologyEcologyPhilosophy

Abstract

fetched live from OpenAlex

Indigenous Communities worldwide stress the vital role of Land in their health and identity. Colonisation has intentionally disrupted this connection; however, Indigenous Communities are reclaiming and reviving their cultures by resisting colonial influences and enacting Indigenous methodologies and pedagogies. This scoping review aimed to understand the ways in which Land-based healing is conducted and understood globally by Indigenous Communities. Two reviewers searched five databases to identify records eligible for inclusion. Principles of content analysis were used to synthesise patterns across the data. The systematic search located 9,018 unique articles, of which 27 fully satisfied the inclusion criteria. Findings represented 13 Indigenous Communities across four countries. The included articles collectively applied a set of seven shared principles in their practice. Based on the evidence discussed in this review, combined with the wealth of global Indigenous Knowledges the significance of Land-based healing for the well-being of Indigenous Peoples is indisputable.

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)0.009
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0160.015
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.078
GPT teacher head0.463
Teacher spread0.386 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations1
Published2025
Admission routes1
Has abstractyes

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Same venueAlterNative An International Journal of Indigenous PeoplesSame topicIndigenous Health, Education, and RightsFrench-language works237,207