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Two Films about Becoming Plant

2021· article· en· W3163533645 on OpenAlexaffvenue
Laura U. Marks

Bibliographic record

VenueAnthropologica · 2021
Typearticle
Languageen
FieldHealth Professions
TopicFilm in Education and Therapy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

Becoming like a plant involves spending time with plants, studying them, mindfully ingesting them, and changing one's scale-both shrinking and diffusing-to occupy the scale of a plant.A fiction film from Korea, Yim Soon-Rye's Little Forest (2018), and a documentary from the United States, Lisa Marie Malloy and JP Sniadecki's A Shape of Things to Come (2020), present creative vocabularies for stories of characters who become-plant.In both movies, the protagonists develop self-sufficient, earth-friendly ways of living and, more or less emphatically, reject the capitalist social order.Each film is supported by beautiful cinematography, arising from the elegant watchfulness of Lee Seung-hoon, and the extraordinary intimacy and trust Malloy and Sniadecki gained with their outlaw character.

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)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0230.002

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 head0.124
GPT teacher head0.499
Teacher spread0.375 · 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
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
Published2021
Admission routes2
Has abstractyes

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