Erasure as a Tool of Nineteenth-Century European Exploration, and the Arctic Travels of Tookoolito and Ipiirvik
Bibliographic record
Abstract
Abstract The American publisher Charles Francis Hall had no previous experience with the Arctic before he travelled there in 1860. Yet, Hall transformed himself into an Arctic authority, and was given command of a United States governmental funded expedition in 1870. Hall was only able to undertake his work in the Arctic because of his relationship with Tookoolito and Ipiirvik, a married Inuit couple from Cumberland Sound, and this article examines the structural processes that enabled Hall to rescript their expertise as his own. Tookoolito and Ipiirvik travelled with Hall for over a decade, a relationship where the unequal power-dynamic was continuously transformed and renegotiated in the United States and the Arctic. Drawing on recent historiographical insights on the construction of exploration knowledge in the imperial context, this article interrogates the epistemic and physical violence involved in Hall's erasure of Tookoolito and Ipiirvik's expertise and personhood. In doing so, I highlight the structural function of the erasure of Indigenous knowledge and labour in the production of nineteenth-century European and Euro-American Arctic science, and its enduring influence on the historiography.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".