Bibliographic record
Abstract
The call sent out for this issue of Journal of Northern Studies asked for contributions on "Language and Place in Northern Spaces," a theme that invites interpretation. 1 What is a "northern space" to begin with?And how is language and place connected?As the contributions were sent in and the issue gradually took shape, possible answers to these questions were formulated.Although a "northern space" is a mental construct whose only necessary characteristic is that of being located north of a given vantage point, there also exists a sort of "canonised" north, i.e. geographical areas and societies "furthest to the north."It is this latter type of northern spaces that became the main focus of this thematic issue: Sami contexts in Sápmi in northern Scandinavia and Inuit contexts in Greenland, Canada and northern Alaska.The juxtaposition of language and place also requires an explanation, since there are many kinds of such connections, illustrated by research on, for example, linguistic landscapes, dialectology and sociolinguistics.In place-name studies, however, there are indisputable and strong connections between language and place.Place-names not only identify places and make it possible to describe and talk about the places they denote, they are place, they create place."Naming," as the geog-
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".