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Record W4205280660 · doi:10.2458/jpe.2399

Monster plants: the vegetal political ecology of <u>Lacandonia schismatica</u>

2022· article· en· W4205280660 on OpenAlexaff
Leticia Durand, Juanita Sundberg

Bibliographic record

VenueJournal of Political Ecology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Cultural Studies in Latin America and Beyond
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPoliticsGeopoliticsIdentity (music)Environmental ethicsEcologySociologyEthnologyPolitical scienceBiologyLawPhilosophy

Abstract

fetched live from OpenAlex

This article presents a story about a plant – Lacandonia schismatica – which subverted disciplinary traditions in botany and reconfigured its geopolitical orders of knowledge. To tell this story, we focus on Lacandonia's 'plantiness', Lesley Head and colleagues' (2012) concept to signify each kind of plant's unique biophysical characteristics, capacities, and potentialities, and through which they co-produce the world. We trace how L. schismatica intervened in, and (re)configured processes of knowledge production, environmental politics, and identity formation in the Lacandon Forest, Chiapas, Mexico, where it was found. Lacandonia's plantiness came into being through sudden macromutations; this unexpected but viable plant species participated in reviving an old debate in evolutionary biology: macroevolution versus gradualism. We also analyze how Lacandonia's plantiness compelled shifts in environmental politics in Chiapas and identity formation in Frontera Corozal, the Chol community where L. schismatica was first located. We conclude with a brief reflection on the implications of vegetal ethics for addressing contemporary environmental crises.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.006
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.222
Teacher spread0.216 · 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 designQualitative
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

Citations11
Published2022
Admission routes1
Has abstractyes

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