Qanilaarneq (Closeness/Being Close) as a Desired State: Mediating Conflict Through Storytelling in Kalaallit Nunaat (Greenland)
Bibliographic record
Abstract
This paper focuses on storytelling as a site for knowledge creation and meaning making to better understand how relationships are established and community is made in Kalaallit Nunaat (Greenland). The basis of this article is a selection of stories created by young Kalaallit (Greenlandic Inuit) adults as part of a research project engaging in future memory work, where participants were asked to create “future memories” for subsequent generations by producing stories that best represent what they consider to be worth preserving. Connecting these stories are the storytellers’ evocations of solidarity, care, and responsibility, gesturing to other members of the Kalaallit community. Rather than centering on the stories’ content, I conceptualize their intent through a focus on storytelling as a social activity. I argue that the young Kalaallit I worked with seek to mediate conflict and mend rifts in society through their storytelling practice, envisioning a future state of Kalaallit community relations that is “closer” in nature, and I propose the Kalaallisut term “qanilaarneq” (closeness/being close) as a metaphor to think with and make this notion more tangible.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".