Predictive Modeling and the Ecology of Hunter-Gatherers of the Boreal Forest of Manitoba
Bibliographic record
Abstract
There are several reasons for archaeologists to develop and critically examine the use of archaeological predictive models (APM). APM has had an immense impact on the field of Cultural Resources Management (CRM), particularly in North America. APM is thought to be much more effective in predicting hunter-gatherer site locations, rather than the site locations of complex societies. It is hoped that by the development and critical assessment of APM that these concerns can be addressed and what is a potentially powerful archaeological tool can gain greater acceptance. In this volume, the author creates four models to predict site locations of boreal forest hunter-gatherers. Two of the models are created using cultural and environmental variables. The third model focuses on economic variables in creating a predictive model using logistic regression, and the fourth is a model that combines economic, cultural and environmental variables to make predictions. Finally, this research tests the effectiveness of general ecological models of cultural behaviour as well as the relative merits of environmental/cultural and economic models. Furthermore, the research will test basic principles of cultural ecology at a time when many anthropologists are in the process of revising and updating this paradigm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".