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Record W7052307741

To the reserve and back again : Kahnawake Mohawk narratives of self, home and nation

2003· dissertation· en· W7052307741 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2003
Typedissertation
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipMohawkNarrativeIndigenousPoliticsEthnographyConstruct (python library)AssertionIndependence (probability theory)
DOInot available

Abstract

fetched live from OpenAlex

This dissertation investigates the social and cultural contours of citizenship and nationhood of Kahnawake Mohawks. The central question that I seek to answer is "What other narratives of nationhood and citizenship are there than those of membership in the American or Canadian states?" Mohawks and other Iroquois nations have long asserted their ideological, and in the case of some, economic independence from the governments of Canada and the United States. My multi-sited research illustrates that this historical assertion is more than rhetoric; it is also a practice or " praxis," as Mohawks configure citizenship across the imposed borders that separate their reserves from cities and states from states. This dissertation engages contemporary theories of nationhood, historical and contemporary ethnographic literature on the Iroquois, as well as contemporary literature in political theory and policy to examine the gendered and sometimes racialized contours of Indigenous nationhood and citizenship across borders. Kahnawake Mohawk narratives and the choices that they entail have implications for the way that all "post-colonial" nationals attempt to imagine and construct their place and their membership within and beyond the boundaries of their communities and that of the state.

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 score0.879
Threshold uncertainty score0.241

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 studies0.0160.016
Scholarly communication0.0060.006
Open science0.0000.004
Research integrity0.0010.002
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 head0.021
GPT teacher head0.270
Teacher spread0.249 · 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

Citations25
Published2003
Admission routes1
Has abstractyes

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