Bibliographic record
Abstract
The Arctic Observing Network–Social Indicators (AON-SI) project, building upon earlier work, developed an approach for linking social with physical science data across the Northern pan-Arctic. The first iteration of social data involves time series of demographic indicators in more than 100 separate regions, such as boroughs of Alaska, census divisions of Northern Canada, and oblast or autonomous regions of Northern Russia. Its geographical scope covers all of Alaska, Greenland, Iceland, the Faroe Islands, Norway, Sweden, and Finland, along with Northern parts of Canada and Russia. Administrative subdivisions within these areas define the regions. Key features of this data framework are (1) the list of region names, (2) a unified system of numerical identification codes, and (3) a region-year organization. We approximated the geographical area of each region as a particular set of 25 × 25 km grid cells, following the Equal-Area Scalable Earth Grid (EASE-Grid) scheme widely used in high-latitude natural science. Physical data in this format are accessible through the Arctic RIMS website. The linkage opens doors for integrated analysis of social and natural-science data, as illustrated by an analysis of Alaska community electricity use. Current versions of the database are published online.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".