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Record W2790361869 · doi:10.1080/14614103.2018.1435981

Extraordinary Creatures: The Role of Birds in Early Iron Age Slovenia

2018· article· en· W2790361869 on OpenAlexaboutno aff
Adrienne C. Frie

Bibliographic record

VenueEnvironmental Archaeology · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
FundersUniversity of Wisconsin-MilwaukeeWenner-Gren Foundation
KeywordsCreaturesQuarter (Canadian coin)Variety (cybernetics)GeographyIron AgeHistoryZoologyArchaeologyBiologyNatural (archaeology)Computer science

Abstract

fetched live from OpenAlex

Depictions of birds are overrepresented in the Dolenjska Hallstatt culture, and appear on over a quarter of artefacts depicting animals. A wide variety of artefacts with birds have been found primarily in graves, and crosscut gender, status, and age. However, poor preservation of zooarchaeological remains has made reconstructions of lived human-bird interactions difficult. This study uses ecological and ethological data, combined with local imagery, to provide insight into prehistoric human-bird interfaces in this area, and the cultural conceptions surrounding these interactions. Birds would have been a constant presence in the lives of Dolenjska Hallstatt people; however, human relationships with them were based more on observation than direct interaction. Birds were ubiquitous in imagery, and it is proposed that this stemmed from Dolenjska Hallstatt conceptions of birds as important observers of human actions, ritual mediators, and possibly guides or guardians. Their differences from humans and other animals distinguished them – they were set apart, and depictions highlighted non-normative behaviours. Birds in the Dolenjska Hallstatt worldview were more than animals, ascribed extraordinary capabilities that made them ritually potent and richly symbolic creatures.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.999

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 studies0.0000.006
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.178
Teacher spread0.174 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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