Bibliographic record
Abstract
A difficulty facing historians of the Arctic is that the names of countries and peoples that appear in the historical record are almost wholly different from those used by the people themselves today.Home rule in Greenland and the creation of Nunavut, the new territory in the Canadian eastern Arctic in 1999, have been accompanied by the renaming of towns and other communities.In a similar fashion Arctic peoples have adopted new words to describe themselves.The term 'Eskimo' has been largely replaced by 'Inuit', but that is a generic name and different sub-groups have adopted other names.Elsewhere in the Arctic the Lapps now call themselves 'Sami', and the Samoyed 'Nentsy'.As the process of change is continuing, it is difficult to create hardand-fast rules, so the following procedures have been adopted.Firstly the people will be referred to by their current preferred name.However, where it is still approprate to use older names in order to make sense of earlier descriptions, they will be used in preference.Secondly the traditional Europeanised country and island names will be used, e.g.Greenland, Baffin Island, and the same will apply to names of seas and fjords, as they are still mostly used by the compilers of atlases.Native names will be used for towns and villages where they exist, as their use is becoming more widespread and they feature increasingly in contemporary literature.However, to help the reader they will be accompanied by their past European names.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.034 | 0.050 |
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".