Intimate Clips: Sealskin Sewing, Digital Archives and the Mittimatalik Arnait Miqsuqtuit Collective
Bibliographic record
Abstract
This article reflects upon the interplay of digital, material, and social relations in the context of a small-scale digital archiving project currently being undertaken by a group of women ethnographers, videographers, and sealskin seamstresses in the Canadian Eastern High Arctic Inuit settlement of Mittimatalik (Pond Inlet). I illustrate the documentation work of our Mittimatalik Arnait Miqsuqtuit Collective, situating it in the new media landscapes that have developed in the Canadian Arctic, and draw on case studies to challenge claims that new communications technology has led to the breakdown of social and environmental relationships. Clips from our digitizing work in progress offer insight into the relational ecologies emergent the making of this archive: illustrating how the unique materiality of sealskin and digital archives, the politics of Inuit hunting, the sensibilities of family and friends, and the challenges of broadband connectivity in Arctic settlements shape this initiative. Technology also emerges here as a key agent, enabling new collaborative relationships, political voice, and forms of knowledge production, but also denying others.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".