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Record W4403157959 · doi:10.29173/comp76

Gender Identity and Mortuary Analysis in Prehistory

2024· article· en· W4403157959 on OpenAlexaffvenue
Robert E. Brown

Bibliographic record

VenueCOMPASS · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPrehistoryIdentity (music)AnthropologyHistorySociologyGenealogyGender studiesArchaeologyArtAesthetics

Abstract

fetched live from OpenAlex

The social-constructionist understanding of gender as the cultural elaboration of sex has been criticized by third-wave feminists for its propensity to essentialize gender and its adherence to a binary, two sex/two gender model. Despite challenges to this hegemonic stance, gender archaeology has yet to become an integral and assumed part of archaeology’s foundational principles and remains at the periphery of disciplinary research. As such, the assumption that this heteronormative framework is both universal and natural remains well entrenched in archaeological mortuary analysis. It is the goal here to deconstruct this familiar and comfortable paradigm and expose the presentism that perpetuates it. As prehistoric graves pose the greatest challenge in assigning gender identities, lacking written documentation and cultural narratives that aid interpretation, the focus here will be to address the challenges of decrypting gender identities in a prehistoric context.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0090.031
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.065
GPT teacher head0.300
Teacher spread0.235 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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