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Record W3009715318 · doi:10.22584/nr50.2020.004

Questions about Questions: Law and Film Reflections on the Duty to Learn

2020· article· en· W3009715318 on OpenAlexaffvenueabout
Rebecca Johnson

Bibliographic record

VenueThe Northern Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsIndigenousDutyObligationCommissionLawSociologyWork (physics)Focus (optics)Political scienceEngineering

Abstract

fetched live from OpenAlex

In 2015, The Truth and Reconciliation Commission concluded that reconciliation will require new relationships between Canadian and Indigenous legal orders. How are legal professionals to participate in making these new relationships? How might lawyers engage productively with the many different Indigenous legal orders in this land? The article takes up challenges of Reconciliation and the Duty to Learn, with a focus on the place of questions in the process of learning. Stories are one important location for this work. Reflecting on the course Law 343: Inuit Law and Film, I offer some thoughts on cinematic stories as a particularly productive site for legal thinking, with a focus on the place of questions as a technique for building understanding and relationship across difference. Using the film The Journals of Knud Rasmussen (2006), I explore six different questions, and consider the kind of work that one can do with each question. This approach invites us to consider the relations we build through the questions we ask, not of others, but of ourselves. I close with some reflections about steps one might take to act on the obligation to learn, taking up the work of questions in our practices of building relations across legal orders.

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)0.001
metaresearch head score (Gemma)0.001
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: Commentary · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.070
GPT teacher head0.369
Teacher spread0.298 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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
Published2020
Admission routes3
Has abstractyes

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