MétaCan
Menu
Back to cohort
Record W7134997748

Imperial Green

2025· other· en· W7134997748 on OpenAlexaboutno aff
Zachary Daniel Lacosse

Bibliographic record

VenueYorkSpace (York University) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsIntrospectionFilm directorIdentity (music)Set (abstract data type)Assemblage (archaeology)Formative assessment
DOInot available

Abstract

fetched live from OpenAlex

Imperial Green is a 66-minute experimental autofiction film by filmmaker Zachary Lacosse, loosely structured as a reimagining of his life prior to relocating to Toronto. Set in Metrotown, a suburban district of Burnaby, British Columbia, the film foregrounds a place that has exerted a formative influence on Lacosse since childhood. Through a personal and introspective lens, the work explores struggles with Christian-tainted identity and the ways these internal tensions shaped his relationships with his mixed-Indigenous father and his fiancée at the time. Conceived alongside his decision to attend York University, Imperial Green reflects Lacosse’s desire to enter the world as an individual, charting the emotional and psychological pitfalls inherent in the process of becoming. Shot in high-framerate 60fps, it is a diaristic retelling of a crucial point in the filmmakers life, as well as an examination on urbanization, the automobile, and masculine impotence.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.269
Threshold uncertainty score0.898

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.001
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2690.036

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 head0.008
GPT teacher head0.192
Teacher spread0.184 · 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 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueYorkSpace (York University)French-language works237,207