Arctic symposium: Perspectives on Gender
Bibliographic record
Abstract
This international symposium brings together Inuit organizations, youth, artists and the academic community to explore the diverse implications and expressions of gender in the Arctic across various domains, including academia, the arts, community outreach, popular culture, and Indigenous knowledge systems. We are pleased to announce that the Arctic Symposium Podcast is now freely available to everyone through the link below. This episode is the result of a collaborative effort between our guest speakers and the symposium’s organizing committee. The narration is presented by Christine Qillasiq-Lussier, a PhD student at Concordia University working on Inuit oral history, featuring music by singer-songwriter Béatrice Deer from Nunavik. This audio capsule revisits key segments from the 2025 international Arctic Symposium: Perspectives on Gender, highlighting ongoing reflections on care as both practice and politics. We’d like to thank Réseau Dialog (Réseau de recherche et de connaissances relatives aux peuples autochtones) and Pauktuutit, Inuit Women of Canada for their generous support, which made this project possible. Guest speakers and contributors: Suzy Basile, Mikaali Bro, Marie Evaldsen Christensen, Aka Hansen, Nooks Lindell, Natasha MacDonald, Carissa Inuujaq Metcalfe-Coe, Diane Obed, Christine Qillasiq Lussier, Lydia Risi, Mette Mørup Schlütter, Christine Tootoo, Karla Jessen Williamson
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".