A game of two halves: Looking for evidence for both embedded and direct procurement in a simulated dataset
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".