Mobility, materiality, and memory: Silas Sandgreen and the construction of Kalaallit cartography in the 1920s
Bibliographic record
Abstract
SummaryIn 1925, Silas Sandgreen sent a map to the Library of Congress in Washington, D.C. Instead of ink on paper, Sandgreen’s map featured strands of sinew binding painted driftwood islands to an animal hide, articulating the islands Kitsissut and Imerissoq of Disko Bay off the western shore of Kalaallit Nunaat. In the near century since its completion, the map’s materials have become indexical of their maker’s Indigeneity, functioning as erroneous evidence of “authentic” Inuit cartographic practices. A repeated fetishizing of alterity has divorced the object from its original conditions of creation, obscuring its origins and cultural meanings.This paper seeks to restore the historicity of Silas Sandgreen’s map by taking a new approach to its materiality. Taking cue from recent scholarship that frames the map as an artwork, we locate the object at the intersection of various social, political, and environmental ideologies sweeping Kalaallit Nunaat, and Sandgreen’s particular home islands, in the 1920s. In order to do so, we restore the maker’s biography composed from new findings in Indigenous-language archives, and juxtapose that biography alongside a visual and material analysis of the most prominent media of the map: sealskin and driftwood. By charting these material histories alongside social and ecological ones, we aim to provide a template that advances multiple interdisciplinary methodologies in the nascent field of Arctic art history.
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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 | 0.015 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".