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Record W6963358865 · doi:10.17613/1b57-wa10

The Great Divide

2024· article· en· W6963358865 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical and Religious Studies of Rome
Canadian institutionsnot available
Fundersnot available
KeywordsBarbarianAntiqueMiddle AgesEthnic groupVisual cultureLate AntiquityIdentity (music)Roman art

Abstract

fetched live from OpenAlex

Ethnicity and identity have formed a major focus in late antique and early medieval archaeology and history. Wide-ranging debates between the so-called Vienna and Toronto Schools have had massive impacts beyond early medieval history, as has the famous project, The Transformation of the Roman World. Here, a new paradigm emerged, slowly substituting the previous ‘decline-and-fall’ ideas of the antique world with that of ‘transformation’. The study of late Antiquity and the early Middle Ages in the Roman West is thus very much entangled with research on identity, ethnicity, and grand narratives, such as transformation or decline, ‘Germanic’ or barbarian invasions. These influential concepts and ideas should not be underestimated in the study of art and visual culture as they too frame the historical scenes in which art history is set. Since the mid-2000s, there have been new debates, mostly (but not solely) triggered by Heather, Ward-Perkins, and Halsall. The question of the extent to which ethnicity has played a significant role in the use of material culture, and to which it can thereby be identified in the archaeological record, has been widely, and often intensely, debated across late antique and medieval archaeology. The research on art and visual culture, however, embarked on a different tangent. Largely ignoring recent debates in history and archaeology, most scholars still emphasise the function of early medieval art and images as fostering perceptions of ‘Germanic’ identity, ethnicity, or religion. But why does the ‘Germanic’ remain such a pervasive terminology?

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.660
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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.031
GPT teacher head0.215
Teacher spread0.184 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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