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Record W4406062247 · doi:10.1016/j.jaa.2024.101654

Making kw’éts’tel: A materialization of household food-focused labor

2025· article· en· W4406062247 on OpenAlexaff
Anthony P. Graesch, David M. Schaepe, Nathan Goodale, Hector Salazar, Moriah McKenna, Sarah Harris, Andrew Prunk, Annette Davis, John Rissmiller

Bibliographic record

VenueJournal of Anthropological Archaeology · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsNative Mental Health Association of Canada
Fundersnot available
KeywordsBusinessAgricultural economicsEconomics

Abstract

fetched live from OpenAlex

Salmon fishing and storage have been integral elements of Stó:lō-Coast Salish household life, economy, and identity in the Fraser Valley and lower Fraser Canyon of southwestern British Columbia for millennia. However, taphonomic factors affecting salmon remains make it difficult to directly study variability in food-related labor allocations, prompting us to focus instead on fish processing tools. This study employs experimental archaeology, archaeological collections analyses, and geochemistry to investigate the production of kw’éts’tel—ground slate fish knives essential to the precontact Stó:lō-Coast Salish salmon economy. Our objectives are to examine the forms and attributes of finished kw’éts’tel blades, explore potential slate sources, and assess decisions, techniques, and labor involved in blade production. Using an integrated methodological framework, our analyses offer nuanced insights into kw’éts’tel production and its role in Stó:lō-Coast Salish social organization. We argue that this approach enhances our ability to interpret the kw’éts’tel-focused archaeological record, shedding light on social change over time. This is particularly significant in a region where the emergence of a high-ranking social elite was partly driven by positioning and placement within the means and mode of production in the salmon-focused fishing economy.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.301
Teacher spread0.253 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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