Bibliographic record
Abstract
Aboriginal settlements 246-65 entrepreneurship, see entrepreneurship and innovation geographic/demographic context 27, 254-8 Australia (remote or very remote) 255, 256, 257 Canada 255, 256-7 mixed-markets 258-60 art 259 beyond homogeneous classifications 252-4 community researchers 259-60 rangers 258, 261, 264 place-based planning and 128-9 social enterprise in Canada cooperatives 260-61 data 248, 249, 262, 264 McMurtry and Brouard's taxonomy 261 qualitative characteristics 260 service delivery 262 western data methods homogeneous classification 251 inadequacies 248, 249, 262, 264 and multi-disciplinary approach 249-50 not reflecting Aboriginal laws and customs 246-8 qualitative 'bridge' needed 262-4 theories of Homo economicus 250-52 see also entrepreneurship and innovation; land rights of Indigenous populations African migrant networks 85-6 age patterns 106, 117, 188-91, 281-3, 297, 302, 305-6, 326 'ageing in place' 67 used as signifiers of 'decline' 428-9 Alaska 25, 28 Anchorage, see Anchorage earthquake (1964) 37-8 Indigenous population demographic profile 216 land rights of Indigenous populations 207, 208-10 claims and title 216 claims legislation 43-4, 208-9 comparison with Northern Territories 215-17 employment 216 native corporation approach 210-11 population patterns 209, 216 resource extraction and commerce 40, 209, 210, 384 transport systems 209 Nome, Kotzebue and Barrow 41-4, 386 Alessa, L. 390 Alice Springs 84-6, 91 Anadyr 385-6 Anchorage 25, 26, 28, 33-41 early history 34-5 economic diversification 38-40 government disaster rescue 36-8 military economy 35-6 oil 40 transport 33-5, 39-40 Appalachian Centre for Economic Networks 381 Arctic regional centres 25, 28, 41-4 Australia 31 Cape York Peninsula 132-40 Daguragu 160-61 Darwin 26, 31, 37-8, 77-8, 211 as LGN hub 220-21, 226, 227-30, 236-9, 241-2 data for settlement level analyses 248 2016 census 171, 172
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.834 | 0.680 |
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".