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Record W1943543473 · doi:10.36368/jns.v2i1.546

Northern Science and Research : Postsecondary Perspectives in the Northwest Territories

2008· article· en· W1943543473 on OpenAlexaboutno aff
Chris Paci, A Hodgkins, Sharon P. Katz, Jazzan Braden, Michael Bravo, Ann Gal Ruth, Cindy Jardine, Mark Nuttall, Joanne Erasmus, Steven Daniel

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsScience educationSociologyPedagogy

Abstract

fetched live from OpenAlex

The International Polar Year (IPY) provides an opportunity to reflect on Northern science and research. For all Canadians, science and research should contribute to living a good life. A good life includes successfully making sense of the world within local contexts, sharing this knowledge beyond the immediate community and reconciling it with knowledge held by outsiders. Northern science and research are inherent in Traditional Dene, Inuvialuit and Metis knowledge; and they continue to be reflected in Northern governance, economy, and cultures. Alongside Aboriginal sciences are Western sciences; these are primarily disciplinary in nature and formally structure postsecondary education globally. Postsecondary science and research education is still being introduced to the Northwest Territories (NWT). Over the last forty years the territorial government has developed the capacity for educational services, funding, institutions, and authority through the Department of Education, Culture and Employment. The delivery of Northern-based postsecondary education through Aurora College provides Northerners with the capacity to generate science and research in the North. What place do science and research have in the North? (North in this paper demarcates the socially constructed geopolitical territories north of the 60th parallel that we use cautiously as a structural term for the purposes of our narrative.) What kinds of investments need to be made and will Northerners be prepared to overcome barriers and take advantage of the opportunities?

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.010
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.311
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueKTH Publication Database DiVA (KTH Royal Institute of Technology)Same topicPolar Research and EcologyFrench-language works237,207