Movement and Common Worlds in Early America:Homelands, Pathways and the Peoples of the Northeast
Bibliographic record
Abstract
This publicly available interactive digital research visualisation is a radically new yet accessible way of understanding the political geography of the American Northeast, 1600-1800. It significantly extends prior mapping projects centred on loss and erasure (Saunt, 2014) or British colonial dominance (Edelson, 2017). It is the first resource-intensive map platform co-produced with Indigenous collaborators and uses advanced digital storytelling techniques to address the urgent transnational call (Blackhawk 2023) for new themes, geographies and chronologies revealing the intercultural complexity of early America. It combines new research (Hämäläinen, 2022, Ellis, 2022, Porter, 2024, Prior, 2026) with leading-edge design and interface functionality to disrupt conventional historical and spatial assumptions and instead foreground Indigenous ways of conceptualising land as ‘cultural and moral space’ (Carson, 2002). Dynamic animations literally re-draw the map, re-orienting understanding around Indigenous placenames, homelands, land-rooted stories, spirituality, and pathways of trade, diplomacy, and conflict. This novel approach and method involved Indigenous co-production at every phase and the geo-rectification and data mining of British Library and Library of Congress historic maps to create a unique database (120,000 data points), organised into a new typology and symbology, time sequenced, and layered onto clean and modern street map templates. Free exploration of intercultural spatial dynamics at publiultiple scales is enabled via four thematic map stories, techniques later adopted by Canadian non-profit Native-Land Digital. As the first modern Indigenous-informed map of the Northeast, this platform is a primary point of reference for scholars, policy-makers, government and heritage professionals. Global provider Gale-Cengage is distributing it within the US college ecosystem. It has profound value for Indigenous communities, particularly those fighting for Land Back in the Northeast. Co-produced with King’s Digital Lab as part of ‘Brightening the Covenant Chain: Revealing Cultures of Diplomacy between the Crown, the Iroquois and their Neighbours’, AHRC Standard Research Grant (AH/T006099/2).
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".