Nunami iliharniq (Learning from the land): Reflecting on relational accountability in land-based learning and cross-cultural research in Uqšuqtuuq (Gjoa Haven, Nunavut)
Bibliographic record
Abstract
The land is where Inuit knowledge transfer has taken place for generations. Land-based programs for learning and healing have been increasingly initiated across Inuit Nunangat in support of Inuit knowledge transfer that was disrupted by colonial settlement policies and imposed governance systems. We worked with Elders in Uqšuqtuuq (Gjoa Haven, Nunavut) to develop a project to understand the connections between caribou and community well-being. They emphasized that Elder–youth land camps are the most effective means for Elders to share their knowledge, for youth to learn, and for researchers to engage in respectful research. We used the Qaggiq Model for Inuktut knowledge renewal as a guiding framework, and we followed the direction of a land camp planning committee to plan, facilitate, and follow-up on three land camps (2011–2013). The Qaggiq Model also outlines the Qaggiq Dialogue as a way of engaging in relational accountability according to Inuit context and values. In this paper, we reflect on the complexities of upholding relational accountability in cross-cultural research — as part of entering into a Qaggiq Dialogue — with particular emphasis on local leadership, ethics and safety, experiential learning, and continuity. Our intention is to help others evaluate the opportunities and limitations of land camps for their own community context and research questions. Inuit tama’nganituqaq ilihaivalau’mata nunamii’lutik. Ublumiuřuq Inuit nunaa’ni humituinnaq nunami ilihainahualiqpaktut nunamiinirmik, inuuhirmi’nik i&uaqhinahuaq&-utiglu qauřimanirmi’nik tunihinahuaq&utik nutaqqami’nut qablunaaqaliraluaqti’lugu Inuktut ilihattiaruiralua’mata. Qauřihaqtit taapkuat hanaqatiqaq&utik inutuqarnik Uqšuqtuurmiutarnik Nunavummi, nalunaiqhittiarahuaq&utik tuktut inuuhuqattiarutauni-ngi’nik, inutuqallu nunami katiqatigiiquři’lutik i&uarniqšaittuu’mat: inutuqarnut ta’na ilihaqtami’nik ilihaijuma’lutik, inuuhuktullu ilihattiatqiřaujungna’mataguuq, qauřihaqtillu ta’na qauřihattiatqijaujungna’mata atuutiqaqtunik inungnut. Atuqtut malik&utik qařginnguarmik pivaallirutaunahuaqtumik atuqtauvaktumik atu’magit, malik&utiglu katimařiralaat inuit pitquřai’nik, pingahuiqtiq&utik nunami katiqatigiingniqaralua’mata ukiut 2011-mit 2013-mut. Taamnalu qařginnguaq atuqtauvaktuq titiraqtauhimařuq nalunaiqhihima’mat iluani qanuq qapblunaat pittiarahuarniqšaujungnariakšaita qauřihaqti’lugit inuit pitquhiagut i&uatqiřauřumik. Tařvani titiraqtut unipkaaq&utik ilaagut atqunarnia’nik pittiarahuaq&utik ilitquhiqaqatigiinngiti’lugit – inuuqatigiigahuaq&utik qařgiqaqatigiiktutut ukunanik atuutikhaqarahuaq&utik hanařut: taamna qauřiharniq inungnit aulatau’luni, pittiarnirlu qanurinnginnirlu ihumagiřauřut, nunami ilihaq&utik, kajuhiinnarungnaqtumik aturahuaq&utik atuutiqaqtunik inungnut. Qauřihaqtit tařvani unipkaaqtut atuqtami’nik ikajurniqaqu’lugu ahiinut nunami ilihaqtittinahuaqtunut ima’natut hanalutik, atuutiqattiarnia’niglu atqunarnarnia’niglu ilaagut, ahiit na’miniq hanajumagutik nunami’ni qauřihaqrumagutik ima’natut pijungna’mata.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.028 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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".