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Record W3208666514 · doi:10.7202/1081808ar

Nunavut Urban Futures: Vernaculars, Informality and Tactics (Research Note)

2021· article· en· W3208666514 on OpenAlexaffvenueabout
Lola Sheppard

Bibliographic record

VenueÉtudes/Inuit/Studies · 2021
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSettlement (finance)UrbanismIndigenousGeographyFutures contractDeindustrializationArcticUrban planningSpatial planningEnvironmental planningArchitectureEconomic geographyEconomic growthPolitical scienceArchaeologyCivil engineeringEcologyBusiness

Abstract

fetched live from OpenAlex

The Canadian Arctic, and Nunavut in particular, is one of the fastest-growing regions per capita in the country, raising the question as to what might constitute an emerging Arctic Indigenous urbanism. One of the cultural challenges of urbanizing Canadian North is that for most Indigenous peoples, permanent settlement, and its imposed spatial, temporal, economic, and institutional structures, has been antithetical to traditional ways of life and culture, which are deeply tied to the land and to seasons. For the past seventy-five years, architecture, infrastructure, and settlement form have been imported models serving as spatial tools of cultural colonization that have intentionally erased local culture and ignored geographic specificities. As communities in Nunavut continue to grow at a rapid rate, new planning frameworks are urgently needed. This paper outlines three approaches that could constitute the beginning of more culturally reflexive planning practices for Nunavut: (1) redefining the northern urban vernacular and its role in design; (2) challenging the current top-down masterplan by embracing strategies of informal urbanism; and (3) encouraging planning approaches that embrace territorial strategies and are more responsive to geography, landscape, and seasonality.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0000.000
Open science0.0000.002
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 head0.162
GPT teacher head0.497
Teacher spread0.335 · 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 designQualitative
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
Published2021
Admission routes3
Has abstractyes

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