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Record W2809076135

The caves of Cantabria: non-figurative cave art in Northern Spain

2017· dissertation· en· W2809076135 on OpenAlexfundno aff
Dustin Riley

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2017
Typedissertation
Languageen
FieldNeuroscience
TopicAesthetic Perception and Analysis
Canadian institutionsnot available
FundersMemorial University of Newfoundland
KeywordsLiteral and figurative languageCaveArtConstruct (python library)Visual artsArchaeologyGeographyLinguisticsComputer sciencePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

This project focuses on non-figurative cave art in Cantabrian (Spain) from the Upper Palaeolithic (ca. 40,000-10,000). With more than 30 decorated caves in the region, it is one of the world’s richest areas in Palaeolithic artwork. My project explores the social and cultural dimensions associated with non-figurative cave images. Non-figurative artwork accounts for any image that does not represent real world objects. My primary objectives are: (1) To produce the first detailed account of non-figurative cave art in Cantabria; (2) To examine the relationships between figurative and non-figurative images; and (3) To analyse the many cultural and symbolic meanings associated to non-figurative images. To do so, I construct a database documenting the various features of non-figurative imagery in Cantabria. The third objective will be accomplished by examining the cultural and social values of non-figurative art through the lens of cognitive archaeology.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.606

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.004
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.292
Teacher spread0.260 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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