Liminality and Change in an Atlantic Borderland: Notre Dame Bay, Newfoundland in the Eighteenth Century
Bibliographic record
Abstract
Notre Dame Bay in northern Newfoundland was an effective cultural, economic and environmental borderland. The Bay was the core homeland of the Beothuk Natives. They practiced a cyclical economic and spiritual life system that revolved around the seasonal movement of salmon stocks and seal and caribou herds. The Beothuk ranged far offshore to harvest birds on outlying islands, and they wintered in the interior at Red Indian Lake. The Notre Dame Bay zone was distinct, climatically and in other ways, from the rest of Newfoundland. The ecoregion of the Bay is the warmest and driest in Newfoundland, for example. The French had exploited the Bay from the earliest years of their long distance North American fishery, and the English began moving north from their base along the long-settled English Shore in the early 1700s. These first English visitors were looking for furbearers to trap, since the Beothuk had chosen not to trade with any Europeans. The floral and faunal biomass of the region made economic settlement viable. Two rivers, Newfoundland’s largest, provided a navigable corridor to the interior with its temperate continental climate, as the Beothuk had long known. The English began to settle in the harbours and coves of the Bay and ultimately squeezed out the commuting French. Deprived of access to the coast and its spiritual and economic resources, the Beothuk retreated to the interior and were extinct by the late 1820s. In recent years, the borderlands perspective has emerged as one way to frame historical questions emerging from an increased interest in transregional themes such as the Atlantic world. Borderlands were zones where newcomers of various ethnic and traditions competed with Natives to form transitory, liminal regions of community formation and cultural metissage. The borderlands perspective as I employ it also privileges the environmental determinants of settlement by both indigenous and European migrants. Borderlands often unfolded in discrete bioregions such as islands, mountain ranges, or river valleys…all ecologically-bounded zones where the experiences of both Natives and newcomers played out in dynamic conversation with nature. Ultimately, borderlands were events, temporary middle-places of imperial rivalry and socio-economic innovation occasioned by the interaction of climate, geography, biota, indigenous human residents, and incoming settlers. As events, borderlands have definable starting and ending points. This chapter will assess the efficacy of the borderlands perspective in generating a synthesized discussion of a specific Atlantic Canadian bioregion. This analytical vehicle can allow for previously unseen contours of interdependent social, economic and ecological change to be exposed. Are ecologically bounded zones of human competition over resources and land/waterscapes a valid unit of analysis for framing transregional historical questions? Additionally, species depletion, human migration, fire, and climate variability are examples of historical events that have modern parallels. Borderland studies can provide public historians with rich material for cultural programming. This is vital in the modern world, were legacy borderland communities are often located in rural and economically challenged areas. The borderlands approach to history can provide modern economic planners with actionable, ethical alternatives for regional development and rural renewal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".