From Path To Portage: Issues Of Scales, Process, And Pattern In Understanding New Brunswick Riverine Trail
Bibliographic record
Abstract
Scholarly questions about the effects of scales of analysis on data interpretation have been particularly pertinent to historical archaeologists, and have arisen in concert with the continuing push to expand the technical and analytical boundaries of the discipline.The advent of Geographic Information Systems (GIS) has facilitated the mapping and analysis of artifact distributions at large scales, as well as the easy revisualization of data in multiple scales.GIS has become the tool of choice for landscape archaeological approaches, which favor regional-scale surveys.While a growing body of literature exists regarding regional-scale analysis of overland trail systems, very little has been written about trail networks in riverine systems.By contrasting regional-scale and bodily-scale analyses of the riverine trail networks identified in the 1899 work of William Ganong, A Monograph of Historic Sites in the Province of New Brunswick, this paper explores the limitations of regional-scale approaches to analyses of movement.I it I argue that the complexity of processes that determine the accessibility of routes, revealed though this analysis of movement on different scales, complicates conclusions drawn from solely regional-scale analyses of paths and trails.LIST OF FIGURES 1. Francis Joseph's 1708 Map of the Portages through the Lakes in the Township of Whiting 2. New Brunswick River Routes and Portage Paths: Difficulty of Traverse due to Seasonal Fluctuation and River Bedding Characteristics 3. Cedar Canoe Splints or "Shoes" 4. Mi'kmaw Canoe Rigged for Sailing 5. Mi'kmaw Big River Canoe 6. "Tribal Emblems" from Mallery 1893 7. Gabriel Aquin's Birchbark Wikhe'gan 8. Map of the Province of New Brunswick in the Prehistoric (Indian) Period, by William F. Ganong 9. New Brunswick River Routes and Portage Paths v
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 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".