MétaCan
Menu
Back to cohort
Record W3038460982 · doi:10.29311/mas.v18i2.3083

The Disappearance of Arthur Nestor: Parafiction, Cryptozoology, Curation

2020· article· en· W3038460982 on OpenAlexaffabout
Kirsty Robertson

Bibliographic record

VenueMuseum and Society · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsWestern University
Fundersnot available
KeywordsExhibitionMonsterArt historyTRACE (psycholinguistics)HistoryArchaeologyPhilosophy

Abstract

fetched live from OpenAlex

This paper considers Beneath the Surface: The Archives of Arthur Nestor, a parafictional exhibition that I curated in 2014 with 16 undergraduate students at Western University, Canada. The exhibition depicted the life of Dr. Arthur Nestor, a professor of Biology who had disappeared from London (ON) in 1975, seemingly without trace. Over the summer of 2014, some of Nestor’s files and artefacts had been discovered during university renovations, and this archive was given to students in Museum Studies to organize and catalogue. As we sorted through the files, it became clear that Dr. Nestor was something of a controversial figure, a man who became an environmental activist in Southwestern Ontario because of his belief that cryptids (lake monsters) lived in Lakes Huron and Erie, and were in need of protection from human-made pollution. As the documents in his file overlapped with our research in the wider sphere, the evidence seemed to suggest that Nestor had left London to join Dr. Roy Mackal, a University of Chicago professor of cryptozoology searching for the Loch Ness Monster. This paper weaves together the tale of Arthur Nestor and the curating of Beneath the Surface with a history of the relationship between natural history museums and cryptozoology, ultimately questioning what parafiction can do in both art galleries and museums.

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)0.004
metaresearch head score (Gemma)0.011
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.076
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0230.027
Scholarly communication0.0110.007
Open science0.0010.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0080.002

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 head0.029
GPT teacher head0.297
Teacher spread0.268 · 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
GenreOther

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

Citations1
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueMuseum and SocietySame topicGeographies of human-animal interactionsFrench-language works237,207