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Record W4387048417 · doi:10.3389/fsufs.2023.1244790

Pop-up restoration in colonial contexts: applying an indigenous food systems lens to ecological restoration

2023· article· en· W4387048417 on OpenAlexafffundabout
Jennifer Grenz, Chelsey Geralda Armstrong

Bibliographic record

VenueFrontiers in Sustainable Food Systems · 2023
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaMitacs
KeywordsIndigenousRestoration ecologyEnvironmental restorationGovernment (linguistics)WetlandColonialismEnvironmental planningGeographyEcologyEconomic JusticeTraditional knowledgeEnvironmental ethicsPolitical scienceEnvironmental resource managementArchaeologyEnvironmental science

Abstract

fetched live from OpenAlex

As environmental injustices and their disproportionate harms to Indigenous communities are increasingly acknowledged, restoration strategies are being deployed widely by environmental NGOs, resource extraction industries, and government agencies. The inclusion of Indigenous communities and their knowledges in restoration efforts are often considered progress in the pursuit of ecological reconciliation. However, in some cases we have observed a lack of meaningful progress as settler colonial prescriptions for land-healing can eschew efforts to decolonize ecological restoration — what we have labeled “pop-up restoration.” We consider two restoration efforts underway in St’at’imc and Quw’utsun territories (Canada) and contrast them with what we are learning alongside the communities’ own values and efforts to reclaim and revitalize food systems throughout forest, wetland, and grassland systems. Utilizing culturally appropriate pathways, we then evaluate how applying an Indigenous Food Systems lens to ecological restoration may provide a framework to remedy pop-up restoration, confronting settler colonial aspirations to transform Indigenous homelands while asserting justice in ecological restoration contexts.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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 score0.492
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.044
GPT teacher head0.345
Teacher spread0.300 · 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

Citations18
Published2023
Admission routes3
Has abstractyes

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