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Record W7053041741

Unsettling: How Euro-whiteness was portrayed to Indigenous school children as superior to Indigeneity through the textual construction of the “Indian” in Missionary texts during the 1830s to 1845 in the Great Lakes Area.

2019· dissertation· en· W7053041741 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2019
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsDeferenceIndigenousPower (physics)PortraitIndigenous cultureRelation (database)
DOInot available

Abstract

fetched live from OpenAlex

During the mid-1800s, a small influx of American Board of Commissioners for Foreign Missions missionaries set up in the areas of Michigan, Wisconsin, and Minnesota where the Anishinaabe people lived and travelled. A nuanced power dynamic existed between the missionaries and Indigenous populations, and it can be argued, neither the Indigenous community nor the missionaries regarded each other with the respect and deference each expected. During this time period, the missionaries endeavored to ‘educate’ any Anishinaabeg that was willing to participate. These missionaries wrote bilingual textbooks in Anishinaabemowin and English from which to instruct the Ojibwe children. Within these educational texts, a portrait is painted. One of heathens and the saved, of savages and the (Eurocentric) civilized, of Indigeneity and whiteness. This thesis will conduct an exploration of the textual construction of the ‘Indian’ in relation to the Euro-white in the 1830s to 1845 and how the missionaries portrayed Euro-whiteness to Anishinaabe and Métis school children as superior to Indigeneity through the use of the mission schools’ teaching materials.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.894
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.017
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.202
Teacher spread0.194 · 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
Published2019
Admission routes1
Has abstractyes

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