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Record W2808848544 · doi:10.1215/22011919-4385471

The Beaver Diaspora

2018· article· en· W2808848544 on OpenAlexaboutno aff
Laura A. Ogden

Bibliographic record

VenueEnvironmental Humanities · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive reframingPoliticsEnvironmental ethicsEcologyAnthropoceneArchipelagoColonialismSettlement (finance)SociologyEthnologyGeographyPolitical scienceBiologyArchaeologyLaw

Abstract

fetched live from OpenAlex

Abstract For decades the role of invasive species has been central to discussions of anthropogenic loss and change. Conceptual debates over whether “native” and “invasive” species are useful to our understanding of dynamic processes of world making have significantly challenged traditional approaches to conservation biology and conservation practices. Yet decommissioning the “invasive species paradigm” requires us to grapple with new ethical and political frameworks for stewarding the Earth in a time of loss. In response, this essay offers a thought experiment. Instead of referring to invasive species, I reframe the migration and settlement of nonhuman beings as diasporas. Doing so illuminates the political complexities of loss and change in Chilean Tierra del Fuego, where I have been conducting fieldwork for the past five years. Integrating approaches from political ecology, multispecies ethnography, and postcolonial theory, this essay focuses on the introduction in 1947 of Canadian beavers into the Fuegian archipelago (now considered the region’s most significant environmental problem). The introduction of plant and animal life is bound up in the apparatus of settler colonialism, as what Alfred Crosby so famously called “ecological imperialism.” Yet, as I explore in this essay, ecological imperialism is not just the remaking of landscapes to look like Europe but also a process of remaking nonhuman life through the constitution of new multispecies assemblages. Finally, this reframing allows me to destabilize the species concept as a stagnant and apolitical category of difference.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.008
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.024
GPT teacher head0.271
Teacher spread0.247 · 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 designObservational
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

Citations48
Published2018
Admission routes1
Has abstractyes

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