Bibliographic record
Abstract
and maintained by Norman Chance, head of anthropology at the Uni versity of Connecticut, this site is an index of links that aims to highlight the history, present and future of the Arctic, including the subarctic regions of Alaska, Canada, Greenland, Iceland, Scandinavia, Siberia, and Russia.The site focuses on natural re sources, history, culture, social equity, and environmental justice.Some of the scholarly content is created by Chance based on his research (hosted on the "arcticcircle" domain) and some is linked to Web pages outside of the site.The Arctic Circle homepage is com prised of five main headings.Each section consists of a collection of links, which are comprised of photographs, maps, articles, and Web pages.The main headings include natural resources, history, and culture, plus a virtual museum and classroom.The virtual museum contains links to art, photography, and anthropology exhibits online.The vir tual classroom contains case studies written with help from Native northerners and other researchers as a means of distance learn ing for college, university, and high school students who wish to learn more about the Arctic.A lefthand menu on the homepage provides links to an introduction to the site, as well as a welcome message from Chance, maps and GIS images, and a link to other resources on the Circumpolar North.A handy feature of the site is the "Search" link in the lefthand navigation menu.This allows the user to search the site for key words through a Google custom search box.This makes the site much more functional,
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.723 | 0.683 |
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".