MétaCan
Menu
Back to cohort
Record W2404388672

Regrounding in Place: Paths to Native American Truths at the Margins

2013· article· en· W2404388672 on OpenAlexvenueno aff
Michael Lucas

Bibliographic record

VenueCanadian journal of environmental education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsMargin (machine learning)IndigenousLifeworldSociologyPlace-based educationPopulationArchitectureEnvironmental educationAestheticsHistoryArchaeologyPedagogySocial scienceArtEcology
DOInot available

Abstract

fetched live from OpenAlex

Margin acts as ground to receive the figure of the text. Margin is initially unreadable, but as suggested by gestalt studies, may be reversed, or regrounded. A humanities course, Native American Architecture and Place, was created for a polytechnic student population, looking to place as an inroad for access to the margins of a better understanding of Native American/First Nations peoples, and to challenge students to recognize the multiple realities of place through a study of Indigenous place from the People’s conceptions and into contemporary society. Place is specific, and develops from competing recognitions of, and reciprocities with, a common givenness. This form of construction and recognition gathers locations, landscape, and architectural constructions, across a myriad of scales and is authenticated via collateral oral, ritual, and material culture as a rich, visceral lifeworld. The author’s personal and philosophic paths that led to place are discussed as well as pedagogy used within the course, including sessions led by Northern Chumash and Playano Salinan Elders. Resume

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)0.003
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: none
Teacher disagreement score0.958
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0270.025
Scholarly communication0.0080.007
Open science0.0010.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0120.001

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 head0.007
GPT teacher head0.258
Teacher spread0.251 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueCanadian journal of environmental educationSame topicIndigenous Health, Education, and RightsFrench-language works237,207