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Record W4211074124 · doi:10.16997/ahip.931

The Monstrous Anthropocene: Imaginary ‘Sea Serpents’ from the ‘Dark Continent’ Reveal an Earlier Baseline for Real Environmental Impacts to African Marine Life

2021· article· en· W4211074124 on OpenAlexaff
Robert France

Bibliographic record

VenueAnthropocenes – Human Inhuman Posthuman · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAnthropoceneBaseline (sea)The ImaginaryDark AgesGeographyAncient historyHistoryOceanographyEcologyGeologyAstronomyBiologyPsychology

Abstract

fetched live from OpenAlex

For conservation biologists, determining the onset of deleterious change through recognising baseline conditions is regarded as being critical for implementing the effective management and restoration of anthropogenically-altered marine ecosystems. In particular, mining information contained within historical anecdotes from non-traditional sources can provide valuable insights about past environmental conditions. The present study demonstrates that careful parsing of eyewitness descriptions of unidentified marine objects (UMOs), considered at the time to have been sea serpents, reveals that the onset of African marine fauna becoming entangled in fishing gear or maritime debris predates, by more than a century, the advent and widespread use of plastic. This work joins other environmental histories in challenging the misconception of a destructive modernity that can be easily differentiated from an exalted past. Such a reinterpretation of what were imagined to be sea serpents joins a new re-evaluation of Mary Shelley’s Creature, both serving as metaphors of monstrosity – one, concerning the walking, the other, the swimming, undead – for social-ecological upheaval during the Anthropocene.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.010
GPT teacher head0.253
Teacher spread0.243 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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