Scotland’s Research & Higher Education Role and Contribution in a Changing Arctic Environment: Final Report
Bibliographic record
Abstract
Over the years, strong educational and research links have been established between Scotland and the Arctic. The scope of Arctic research in Scotland extends beyond natural and physical sciences to the humanities and social sciences, with politics, law, environment, energy development and economics gaining increasing prominence in recent years. Yet, despite the many research strengths of Scotland-based institutions, much of the Scottish research community remains fragmented. This report is a consolidation of findings from discussions between Scotland-based researchers during a series of network events titled “Scotland’s Role and Contribution in a Changing Arctic Environment”. The network series aimed to lay the foundation for consolidating Scottish expertise in Arctic matters and building a network for more consistent involvement of Scottish institutions in international research with Arctic nations. <br/><br/>To galvanise the strengths that Scottish-based institutions have to offer to the Arctic and vice-versa, Glasgow Caledonian University, in partnership with the University of the Highlands Islands and the University of Aberdeen (the three Scottish members of the University of the Arctic, or UArctic at the time), held a series of 9 themed network events and 2 consolidation events in February and March 2021. The Scottish Government’s Nordic and Arctic Unit provided financial support for the series.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.011 |
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".