Predictive modeling and the ecology of hunter-gatherers of the boral forest of Manitoba
Bibliographic record
Abstract
This dissertation examines the practice of archaeological predictive modeling. The focus in regards to predictive modeling is on two main areas - predictive modeling methodology and the predictor variables employed. Two predictive modeling methodologies are tested using the same set of data. Two cultural-environmental models are created, one using the CARP methodology (Dalla Bona: 1994a, b), and the other employing logistic regression. This allows for the comparison of two distinctly different approaches to predictive modeling. The test of predictor variables is accomplished through the use of environmental data (slope, aspect, distance to lakes/rivers and tree type) in tandem with cultural land-use data (vegetative, earth, local, faunal, ceremonial and industrial resources, trails, and place names). Economic variables (moose and woodland caribou habitat) are also employed. The test of predictor variables is done through the creation of three models using logistic regression: 1) a cultural-environmental model, 2) an economic model and 3) a cultural-environmental-economic model. Each of these models is evaluated using a set of tools: 1) a survey statistic; 2) the Kolmogorov-Smirnov statistical test of significance and 3) the gain statistic (Kvamme 1988a). This allows for an assessment of each of the models' predictive efficacy, and therefore an evaluation of the predictor variables employed in the creation of those models. This assessment allows for comment on the implications of this research for anthropology, for archaeology and predictive modeling, for First Nations communities and for resource companies and cultural resource management.
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 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 | 0.000 | 0.000 |
| 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".