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Record W4293026899 · doi:10.1515/janeh-2021-0016

Defining Marginality and Liminality for the Study of the Ancient Near East

2022· article· en· W4293026899 on OpenAlexaff
Shawn Bubel

Bibliographic record

VenueJournal of Ancient Near Eastern History · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsLiminalityOptimal distinctiveness theoryEmic and eticAnthropologySociologyHistoryMiddle EastArchaeologyPsychologySocial psychology

Abstract

fetched live from OpenAlex

Abstract Marginality and liminality are concepts that are used in the study of the ancient Near East, although formal definitions for these terms are rarely given. This paper explores the history of the uses of these terms in social theory, discussing the origins of the terms in the writings of early twentieth-century thinkers such as Georg Simmel, Robert Ezra Park, and Victor Turner. It details how these ideas and concepts were debated by psychologists, sociologists, and anthropologists throughout the twentieth century, when these terms were adopted into Near Eastern studies. While these concepts were defined by social theorists, investigations of the archaeological, textual, and art-historical evidence from the ancient Near East show that they well reflect emic conceptualizations. Examples from Near Eastern contexts demonstrate the benefits of using flexible and multi-vocalic conceptualizations of marginality and liminality that still recognize the distinctiveness of these two issues. The paper concludes by offering different frameworks for applying these concepts to the study of the Bronze Age Near East.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.208
Teacher spread0.179 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2022
Admission routes1
Has abstractyes

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