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Record W6986268887

Parasitos: reimagining a northern hydroelectric landscape

2022· dissertation· en· W6986268887 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2022
Typedissertation
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousFood securityHydroelectricityFood sovereigntyNatural resourceFood insecurityFood systemsNatural (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

Northern Indigenous communities in Manitoba suffer from food insecurity as a direct result of Manitoba Hydro development along the Nelson and Churchill River systems, watersheds, and tributaries, which has altered the landscape and eliminated traditional food gathering possibilities. Food insecurity has risen in these communities for decades as Manitoba Hydro continues to construct more hydro-electric generating stations while local Indigenous communities call for compensation and mourn the continued loss of natural environments and traditional locations. Therefore, to ascertain how to promote food security and facilitate future autonomy over the land and food production, I conducted a literature review to understand methods of implementing these changes in northern Indigenous communities. The literature review resulted in a site design near the community of Fox Lake Cree Nation (Makaso Sakahigan) and the Limestone Generating Station, which used perpetual waste heat from the generating station to facilitate soap bubble greenhouses sheltered in an abandoned quarry pit to enhance food security for the community. The site design aimed to facilitate more than food security by promoting intergenerational activities and immersion in the natural environment as additional crucial factors in enhancing food security and sovereignty in Indigenous communities.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.439

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.246
Teacher spread0.236 · 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 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
Published2022
Admission routes1
Has abstractyes

Explore more

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