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Record W3124637654 · doi:10.4324/9780429321863-12

Rock art and relational ontologies in Canada

2021· book-chapter· en· W3124637654 on OpenAlexaboutno aff
Dagmara Zawadzka

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicArchaeology and Rock Art Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

This paper explores the ontological evolution of the use of ethnographic observations and data to improve our understanding of prehistoric rock art. In Europe, the use of ethnographic information in rock art research dates back to the early discoveries of prehistoric art at the end of the nineteenth century. But the value given to ethnographic observations today has changed significantly. The original use as a primary source for analogical reasoning based on a purely comparative approach (especially in the area of rock art interpretation) has proven to be unsuitable. Through the establishment of direct connections between cultures that were considered ‘similar,’ in spite of being distant in time and space, several reductionist interpretations of the meaning of European Paleolithic rock art were suggested (including totemism, hunting magic, or shamanism). These theories were also eventually applied to other rock art traditions in this, and other, parts of the world. This use of analogical reasoning is today met with scepticism. In this paper I present an argument for the continuing use of ethnographic data to interpret the rock art of past societies, provided that it is based on an ontological shift in the use of ethnographic analogies. Through a comparative analysis of European and Australian rock art traditions, I will discuss how a controlled use of analogies can be used to critically reflect and test archaeological interpretations in European rock art, to provide alternative interpretations, as well as to contribute to our understanding of the processes of creation and use of past rock art.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.945
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.255
Teacher spread0.224 · 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 designTheoretical or conceptual
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

Citations5
Published2021
Admission routes1
Has abstractyes

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