Bibliographic record
Abstract
If the Arctic is screaming, it's hard for management studies to hear.A search using the word 'Arctic' in the archives of the Academy of Management Journal, Academy of Management Review, Organization Studies, Organization Science, Administrative Science Quarterly reveals a blank space.Maybe you haven't noticed -this isn't a premiere destination for management school faculty.But its absence keeps us -a Canadian and Russian transdisciplinary team -awake at night.Russians refer to the Arctic (ffhrnbra) as 'RhaØybØ edeh' (Far North) and 'þagjkzhüe' (Beyond the Pole).Canadians call it the 'Great White North'.The Saami, Nenets, Khanty, Evenk, Chukchi, Aleut, Yupik and Inuit call the Arctic 'home'.The oil and gas industry call it the 'Last Frontier'.But management scholars don't tend to call it anything.We seek to change that.This may not be easy.Like any discipline, management scholars have well-trodden paths, and ours rarely goes north of 608.Yet scientists from other disciplines (oceanography, biology, ecology, climate, anthropology, glaciology) love the thrill of the remote.From our own experiences and through shared storytelling, we recognize that research in the Arctic is not for the fainthearted.We know numerous scientists who got stranded, faced polar bears, survived submarine fires under the ice and helicopter crashes on land.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".