MétaCan
Menu
Back to cohort
Record W4408674984 · doi:10.70190/jq.i95.p112

On Settler Colonialism, Environment, and Health

2023· article· en· W4408674984 on OpenAlexaboutno aff
Osama Tanous

Bibliographic record

VenueJerusalem Quarterly · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRace, Genetics, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismHistoryArchaeology

Abstract

fetched live from OpenAlex

The Pima Indians of Arizona offer one of the clearest examples of the connections between settler-colonial environmental engineering and health.The community suffers from one of the highest rates of diabetes in the world, with over half of the adult population diagnosed with the disease. 1 In 1877, the U.S. Congress passed the Desert Land Act, allowing settlers to claim arid or semiarid "public" lands in exchange for irrigating and cultivating them, thereby expressing in legislation a settler-colonial logic that views the frontier land as arid, in need of irrigation to produce crops and profit for settler communities.Subsequent "developmental" projects based on such reasoning, like the Roosevelt Dam (1903) and the Florence Diversion Dam (1922), have decreased the Pimas' water access by more than 60 percent, 2 caused irreversible damage to their food sovereignty and lifestyle, and introduced highly-processed market food with high sugar and fat content -a diet that has led to an epidemic of obesity and diabetes.Similar patterns can be seen among indigenous peoples across Canada, Australia, and New Zealand, where settlercolonial projects shattered the fabric of societies and destroyed indigenous farming, fishing, and food gathering practices, contributing to epidemics of obesity, hypertension, and heart diseases. 3espite following a clearly similar pattern, Palestinian health is often excluded from discussions

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.269
Teacher spread0.255 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJerusalem QuarterlySame topicRace, Genetics, and SocietyFrench-language works237,207