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Record W6965533328 · doi:10.35010/vcft-0d95

Ri’pin’tsas ílti skvlhta, ílti spl’úkwa muta7 i skcúsa. Raised in mud, wood smoke, and tears: Reclaiming Indigenous Identity Post Sixties Scoop.

2024· article· en· W6965533328 on OpenAlexaff

Bibliographic record

VenueEmily Carr University of Art and Design Repository · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsEmily Carr University of Art and Design
Fundersnot available
KeywordsNarrativeIdentity (music)IndigenousLiminalityColonialismStorytellingApprehensionGenocidePraxis

Abstract

fetched live from OpenAlex

Raised in mud, wood smoke, and tears is a personal exploration through research/praxis that investigates ways of re-connecting to oral storytelling and land-based forms of knowledge that were ruptured by the Sixties Scoop. My praxis involves the unfolding of an intergenerational narrative that is presented: both my mother, Maria Mae Pascal, and my late grandmother, Theresa Attsie Pascal of the Lil’wat Nation are survivors of the Sixties Scoop which was the mass apprehension of Aboriginal children from their families into the child welfare system that began during the 1960’s and is said to have continued until the 80’s, but I believe it continues still today as our children are still being taken. This thesis is a response to the profound losses left from this part of my colonial history; the land-based elements are representations of these places of disconnection and reconnection. It is an MFA journey that grapples with the intergenerational effects of cultural genocide but is also a story about the reclamation of ancestral heritage. This thesis support document tracks the forces and processes of my research and final MFA project. It is a multi-sensorial installation that invites viewers to inhabit the space and feel the loss and hope that is being evoked through the work.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.621

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 studies0.0010.000
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 head0.017
GPT teacher head0.196
Teacher spread0.179 · 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 designObservational
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
Published2024
Admission routes1
Has abstractyes

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