Highlights from ArcticNet’s Arctic Change 2020 Conference
Bibliographic record
Abstract
2020 was a year like no other for Arctic research, and ArcticNet’s Arctic Change conference was no exception. Held every three years in different Canadian locations, the international conference shifted to a virtual setting with the global COVID-19 pandemic, with 1600 attendees tuning in online from across Canada and around the world. This year included 327 Northern participants, the most representative Arctic Change conference yet. The heart of any conference is the people, and the connections participants make with each other. Going virtual meant giving up the in-person visits, but not the interactions or networking opportunities. Participants watched more than 346 presentations, joined in live question and answer sessions and online chats with panelists and speakers, connected to each other on the virtual conference platform, and more than 5207 streamed the plenaries together. During the week, sessions and conference events were viewed more than 25000 times. The ArcticNet Students’ Association held their annual student day with over 300 participants, finding innovative ways to keep the social spirit of past conferences, holding a virtual trivia night to cap off a busy day of student-focused programming.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.015 |
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".