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Record W6999873056

Documenting Oral History and Lessons in Truth Telling in in Nadia McLaren’s <i>Muffins for Granny</i> and Tim Wolochatiuk’s <i>We Were Children</i>

2017· article· en· W6999873056 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMultilingual Matters (Channel View Publications) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsPerformative utteranceFilmmakingStorytellingOral historyNarrativeIndigenousHarmony (color)Power (physics)
DOInot available

Abstract

fetched live from OpenAlex

While fictional and non-fictional writing on Indian Residential Schools (IRS) has become an important part of the academic landscape well beyond the confines of Canada, documentary filmmaking on IRS has not yet been met with the same level of scholarly attention. This essay on Nadia McLaren’s <i>Muffins for Granny: Stories from Survivors of the Canadian Residential School System</i> and Tim Wolochatiuk’s <i>We Were Children</i> seeks to reduce this divide. As a powerful form of truth telling, these documentaries testify to the power of oral history on par with indigenous storytelling practices and oral traditions, but they take highly different approaches to the sharing of the testimony of residential school survivors and their traumatic memories. McLaren artfully fuses the participatory mode of documentary filmmaking with the balance and harmony of an Aboriginal worldview. Wolochatiuk takes a more controversial approach, stretching the borders between fact and fiction with his highly affective brand of performative documentary filmmaking.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.340
Teacher spread0.296 · 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