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Record W6926603424 · doi:10.25446/oxford.20239503

Law through Film, St. John's University

2022· other· en· W6926603424 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Oxford · 2022
Typeother
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsnot available
Fundersnot available
KeywordsSyllabusPresentation (obstetrics)Class (philosophy)Reading (process)Power (physics)Value (mathematics)

Abstract

fetched live from OpenAlex

Film has the power to stimulate debate. This seminar affords an opportunity to explore jurisprudential issues and value systems through a critical examination of the narrative, historical context, and cinematic technique of films. Thus, this seminar explicitly challenges settled assumptions about law and justice. The films and accompanying reading assignments concentrate on three overlapping themes: defining community, apportioning fault, and distributing justice. In particular, the course highlights the lawyer's role as an "insider" with respect to these concerns, and evaluates the benefits and obligations conferred by that status. When offered during the Fall and Spring semesters, grades are based on two short papers, a research paper, presentation of the paper, and participation in class discussion. When offered during an intersession, grades are based on a final exam, discussion pieces, and class participation. This information has been collected for the Post-Discipline Online Syllabus Database. The database explores the use of literature by schools of professional education in North America. It forms part of a larger project titled Post-Discipline: Literature, Professionalism, and the Crisis of the Humanities, led by Dr Merve Emre with the assistance of Dr Hayley G. Toth. You can find more information about the project at https://postdiscipline.english.ox.ac.uk/. Data was collected and accurate in 2021/22.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.262
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.2620.000

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 head0.015
GPT teacher head0.217
Teacher spread0.201 · 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
GenreOther

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
Published2022
Admission routes1
Has abstractyes

Explore more

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