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Record W1892872966 · doi:10.25071/1920-7336.38168

Lake St. Martin First Nation Community Members’ Experiences of Induced Displacement: “We’re like refugees”

2014· article· en· W1892872966 on OpenAlexafffundvenueabout
Shirley Thompson, Myrle Ballard, Donna Martin

Bibliographic record

VenueRefuge Canada s Journal on Refuge · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLivelihoodRelocationRefugeeMiamiHomelandFirst nationFlood mythCitizen journalismGovernment (linguistics)Environmental planningGeographyPolitical scienceIndigenousPoliticsAgricultureArchaeologyLawEcology

Abstract

fetched live from OpenAlex

In 2011, a massive flood occurred in the Canadian province of Manitoba, and provincial government officials decided to divert water to Lake St. Martin and First Nation land to protect urban, cottage, and agricultural properties. As a result of this artificial flood, all community members were evacuated, with infrastructures and housing at Lake St. Martin First Nation permanently destroyed. Three years later, 1,064 Lake St. Martin First Nation members reside in urban hotels and other temporary residences. Data from participatory videography and community workshops were analyzed using the sustainable livelihoods framework. Environmentally and developmentally induced displacement transformed an entire First Nation community into refuges in their homeland. Jurisdictional issues and racism prevented provisioning of services to meet their basic needs, help rebuild their lives, and relocate their community. Inclusive evacuation, relocation, and water-management policies and procedures are recommended.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.714

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 studies0.0210.010
Scholarly communication0.0040.002
Open science0.0010.007
Research integrity0.0020.004
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 head0.095
GPT teacher head0.316
Teacher spread0.221 · 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

Citations24
Published2014
Admission routes4
Has abstractyes

Explore more

Same venueRefuge Canada s Journal on RefugeSame topicClimate Change, Adaptation, MigrationFrench-language works237,207