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Record W4393389902 · doi:10.1017/aaq.2024.7

Thematic Analysis of Indigenous Perspectives on Archaeology and Cultural Resource Management Industries

2024· article· en· W4393389902 on OpenAlexafffundabout
Alec McLellan, Cora A. Woolsey

Bibliographic record

VenueAmerican Antiquity · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsUniversity of New BrunswickTrent University
FundersMitacs
KeywordsIndigenousThematic mapArchaeologyGeographyResource (disambiguation)HistoryAnthropologyEnvironmental resource managementSociologyCartographyComputer scienceEcologyEconomics

Abstract

fetched live from OpenAlex

Abstract This article explores Indigenous perspectives on archaeology in Canada and the United States and the role of archaeologists in engaging with Indigenous communities. As part of our study, we interviewed Indigenous community members about their experiences in archaeology and their thoughts on the discipline. We analyzed each interview thematically to identify patterns of meaning across the dataset and to develop common themes in the interview transcripts. Based on the results of our analysis, we identified six themes in the data: (1) Euro-colonialism damaged and interrupted Indigenous history, and archaeology offers Indigenous community members an opportunity to reconnect with their past; (2) archaeological practices restrict access of Indigenous community members to archaeological information and archaeological materials; (3) cultural resource management (CRM) is outpacing the capacity of Indigenous communities to engage meaningfully with archaeologists; (4) the codification of archaeology through standards, guidelines, and technical report writing limits the goals of the discipline; (5) archaeological methods are inconsistent and based on individual, or company-wide, funding and decision-making; and (6) archaeological software offers a new opportunity for Indigenous communities and archaeologists to collaborate on projects.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.340

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 studies0.0000.001
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 head0.057
GPT teacher head0.280
Teacher spread0.223 · 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 designQualitative
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

Citations4
Published2024
Admission routes3
Has abstractyes

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