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Record W4390711571 · doi:10.2218/jls.7248

A game of two halves: Looking for evidence for both embedded and direct procurement in a simulated dataset

2023· article· en· W4390711571 on OpenAlexaff
Peter Mears, Lucy Wilson

Bibliographic record

VenueJournal of Lithic Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsProcurementComputer scienceOrder (exchange)Assemblage (archaeology)Raw dataOperations managementArchaeologyOperations researchBusinessHistoryEngineeringMarketing

Abstract

fetched live from OpenAlex

The concepts of embedded and direct procurement have become weighted with extra baggage over the years. In embedded procurement, lithics are obtained along with other resources, while direct procurement involves a deliberate trip to the source for the sole purpose of obtaining that raw material. Lewis Binford suggested that direct procurement means something went wrong (a sign of poor planning), and that embedded procurement is the norm. Other authors found valid reasons why direct procurement could be deliberate, planned, and beneficial. Regardless, the two have often been seen as diametrically opposed, and applied to interpretations of mobility and lithic procurement as if they are mutually exclusive of one another. They have also been variously conflated with expedient and curated technology, the use of local vs. exotic raw materials, and so on. The often site-centric vision of archaeologists (we find it hard to see that people may have been passing through a site, not based there and going out and coming back), can further confuse the issue. The most important problem, however, is: how can we tell the difference between embedded and direct procurement from the stone tools collected at an archaeological site? We created the scenario of a site with various proportions of stone tools from different sources. In order to not influence the site characteristics through a priori expectations, we randomly assigned source qualities and percentages in the assemblage, along with the distances and directions of each source relative to the site. Then each author analysed those data from one of two points of view: LW convinced in advance that the evidence supported embedded lithic procurement, and PM equally certain that a direct strategy was apparent. In both cases, the authors felt they had sufficient “justification” to bolster their point of view and build a strong case for their raw material procurement strategy. This exercise gave some insight into the usefulness and limitations of these two concepts as heuristic devices, as they continue to be a major influence on anyone trying to interpret lithic procurement.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.186
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.215
GPT teacher head0.483
Teacher spread0.268 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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