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Record W4415374917 · doi:10.1017/s0080440125100479

Possible Maps: Newfoundland, 1763–1829

2025· article· en· W4415374917 on OpenAlexaboutno aff
Julia Laite

Bibliographic record

VenueTransactions of the Royal Historical Society · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismIndigenousCoherence (philosophical gambling strategy)StorytellingHegemonyReinterpretation

Abstract

fetched live from OpenAlex

Abstract Islands have a disproportionate role – as strategic locations, as imaginative or symbolic locales, as extractive zones and as ecological bellwethers – in oceanic imperial histories. They were and are places of ‘great practical use and metaphorical power’. And yet Newfoundland was seen (and continues to be seen) as marginal and peripheral, even if the biomass that was pulled out of its ocean fed – quite literally – a global network of exploitation. This article uses four overlapping maps to tell four overlapping stories: James Cook’s circumnavigation of the island in 1763–8; Lt David Buchan’s trek into the interior to contact the Beothuk in 1811 and 1820; William Eppes Cormack and Joseph Sylvester’s trek across the island in 1822; and finally, a series of story-maps created by Shanawdithit, who is apocryphally known as ‘the last of the Beothuk’. In doing so, it draws in Indigenous ‘storywork’ and cartographic histories and makes a case for storytelling as powerful methodology for examining overlooked colonial histories. These maps and stories highlight the complexity of encounter with a place rather than a coherence of colonial ideologies. Through the stories these maps help me tell, I hope to show how the peripheries of some people’s empires were the centres of other people’s worlds.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0090.004
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0170.001

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.

Opus teacher head0.015
GPT teacher head0.207
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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