Tânna Suliatsagijangit Nunatsiavut Taimangasuanit katingaKatigejut Pivalliatitsigiamut Nunalimmi Itsasuanittanik
Bibliographic record
Abstract
Tânna Nunatsiavut Taimangasuanit katimaKatigeKattajut, sakKititauKattajuk jâri tamât taimanganit 2010 ammalu kamagijauKattajut taikkununga Nunatsiavut kavamakkut Itsasuanitanik SuliaKapvinganut, katiutitsiKattajut inunnik ilauKatauKattajunut taimangasuaniusimajunut suliaKaKattajunut pisimajunit ilonnainit annigusuKattajunut Inuit Nunalinnit ammalugiallak kinakkutuinnanut KanuttogutiKaKattajunut uKâlautiKagiamut pitjutaujunut isumâlotigijaujunut ammalu sakKititsigiamut tugâgutitsanik taikkuninga kamagunnagiamut. Sukkajumik taimâk akKutiKaliaKisimajut kinakkutuinnanik ilautitsigiamut ilinganiKajunut taimangasuaniusimajunik pitjutaujunut ammalugiallak pannaigutiliugiamut ammalu ottugagiamut itsasuanitanik iluani nunagijaujunmut. Taikkua katimaKatigeKattajut atjigengitunik nunalinni jâri tamât, pivitsaKattisigiamut ullusiugiamut atjigengitunik piusigijaujunut atunik nunalinni ammalu sivulliutitsigiamut ilinganiKaluattunik atuKattagialinginnik. Itsasuanitak, tânna Kinugautigijaujuk nalunaittauluajunut nunalimmi suliatsanut, ammalu Kanuk suliagijaugajammangâmmik, uKâlautigijausimajut tamât katimaKatigeniammata ullumimut. Jâringani 2015, una SSHRC-ikajutsisimajut ikajuttigetlutik tuniggusiammik akungani Nunatsiavut kavamakkut ammalu Memorial Ilinniavitsuanga taijaujumut PiusituKak ammalu Asianottauvalliajut Akungani Labrador Inunginnut sanajausimajut kamagiamut nunalimmi taimangasuaniusimajunik isumâlotigijaujunut ammalu Kanuttogutigijaujunut sakKititausimajunut taikkutigona katimatsuaKattasimajunut ikajuttigenikkut itsasuanittaligijiujunut ammalu asigiallanut taimangasuanit ilisimallagijunut nunalinnut ikajugiamut Nunatsiavummik angutigiamut taikkuninga tugâgutinnik. Ullumi, ikajuttigennik matukasâlimmat, tunumut takuniavugut sunait pijagettausimammangâmmk, sunait ilinniatausimajut, ammalu Kanuk sivuppiaKatigegajammangâtta.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.010 |
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".