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Record W3116477427 · doi:10.1111/anhu.12306

Seeing from Below: Scuba Diving and the Regressive Cyborg

2020· article· en· W3116477427 on OpenAlexaff
Justin Raycraft

Bibliographic record

VenueAnthropology & Humanism · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsMcGill University
Fundersnot available
KeywordsScuba divingAestheticsNarrativeEthnographyPerspective (graphical)UnderwaterIndexicalitySociologyHistoryEpistemologyVisual artsArtOceanographyAnthropologyLiteratureGeologyArchaeologyPhilosophy

Abstract

fetched live from OpenAlex

Summary This article reimagines scuba diving as a form of ethnographic immersion that allows humans to experience life on earth from an underwater perspective. I argue that scuba divers are both posthuman in their cyborgian transcendence of the basic limitations of our species and prehuman in their metaphorical regression into womb‐like oceans, from which all life on earth evolved. I conceptualize divers as (p)reborn humans. Underwater, symbolic modes of human communication devolve to iconic and indexical forms of gesturing that are more in tune with surrounding ecosystems. Scuba diving involves shamanic navigation between lifeworlds, processes that are deeply ritualistic and psychoanalytically significant. Through first‐hand narrative accounts, divers bring new forms of relational knowledge into the public sphere. Bearing in mind the politics of being underwater, I contend that scuba diving can foster a change in how humans apprehend the ocean, from top‐down representation to bottom‐up lived experience. Scuba diving reshapes attention in ways that could inspire collective appreciation for the blue planet that sustains us. Against the backdrop of global environmental change, I call for a shift in the way that humans see and think about the ocean, from above water to below.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.007
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.334
Teacher spread0.302 · 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; both teacher heads agree on what is shown here.

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
Published2020
Admission routes1
Has abstractyes

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