Bibliographic record
Abstract
In 2019 the University Library at UiT - The Arctic University of Norway partnered with the Arctic Council Indigenous Peoples' Secretariat to create Ságastallamin - Telling the Story of Arctic Indigenous Languages, an interactive multi-media exhibition commemorating the UN International Year of Indigenous Languages. An Arctic Indigenous language map was updated for use in the exhibition, but the print format proved to be limiting, and the exhibition time frame did not allow for a thorough linguistic review. Therefore a follow-up project “Arctic Indigenous languages and revitalization: an online educational resource” was started in 2021 to further develop the map and convert it to an openly available online educational resource in GIS format. One key aspect of the new map is that it will feature individual language names in their Indigenous names, as well as in English and Russian. The project team consists of representatives from Higher Educational Institutions, Indigenous organizations, and Government departments from Canada, Finland, Greenland, Norway, Russia and the United States. The work on the map has just begun and this spring and summer the goal is to collect feedback from Indigenous language experts on the original exhibition print map before designing a new GIS version in the fall. We would like to present the current print map to the Arctic Knot conference in order to receive feedback about additional languages to include, creative ways to present language information in different layers, and suggestions for innovative language revitalization initiatives to feature on the map. There is an online form to collect feedback, and so the lightning talk would be used to present the project goals briefly and then encourage conference participants to submit feedback using the form after the talk.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".