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Record W3089399668 · doi:10.3138/cart-2019-0013

Embodying Emergence: Reclaiming ȽÁU, WELṈEW

2020· article· en· W3089399668 on OpenAlexaffvenueabout
M. A. Neilson, Justin Charles, Kingston Daniels, Laura George, Jorja Horne, Natasha James, Tyrell Jimmy, Danaya Sam, Demetrius Sam, Liam Sam, Mateaya Sam, Richard Henry-Williams, Lorena Smith, Michelle Thomas, J. A. Underwood

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsCapital Regional District
Fundersnot available
KeywordsIndigenousToponymyGeographyJoinsVisual artsHistorySociologyMedia studiesArchaeologyArtComputer scienceEcology

Abstract

fetched live from OpenAlex

W̱SÁNEĆ territory, located on what is known today as the southern part of Vancouver Island, is a storied landscape, holding within its geographical features many sacred places with their own SENĆOŦEN names and stories. For the Grade 3 students of the ȽÁU, WELṈEW̱ Tribal School, a visit to ȽÁU, WELṈEW̱, the mountain that bears the same name as the school, unearthed an unfamiliar layer of the stories connected to this place, which has long been held sacred to the W̱SÁNEĆ people. In the voices of these students, who are now in Grade 4, this article recounts their trip to the ȽÁU, WELṈEW̱ mountain in June 2018 and the process of toponymic activism that followed. The students’ Grade 3 teacher, a Euro-settler educator, joins the students in co-authoring this article, which illustrates why reclaiming Indigenous place names is important to decolonizing the map, first locally for these students and this particular place, and then in a broader sense.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.012
Scholarly communication0.0060.006
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.332
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueCartographica The International Journal for Geographic Information and GeovisualizationSame topicIndigenous Health, Education, and RightsFrench-language works237,207