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Record W3166012680

Declaraciones Asertivas y Paisajes Culturales Indígenas: Apoyo a la Gobernanza Tradicional Indígena en Canadá a Través del Patrimonio Cultural Inmaterial

2021· article· es· W3166012680 on OpenAlexaff
Agnieszka Pawlowska Mainville

Bibliographic record

Venue"PATRIMONIO": Economía Cultural y Educación para la Paz (MEC-EDUPAZ) · 2021
Typearticle
Languagees
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsHumanitiesPolitical scienceArt
DOInot available

Abstract

fetched live from OpenAlex

Dieser Artikel beschreibt die traditionelle Ressourcenverwaltung in indigenen Kulturlandschaften erortert. Durch die Anwendung des Rahmens des immateriellen Kulturerbes in diesem Kontext wird erlautert, wie es als Mechanismus zur Unterstutzung der Territorialitat und der Rechte der Ureinwohner in Kanada dienen kann. Anhand von Beispielen fur drei Kulturlandschaften, die unterschiedlich „geschutzt“ sind, wird in diesem Papier erlautert, wie die ubliche Regierungsfuhrung, ein Element des indigenen Kulturerbes, die zugrunde liegende Masnahme zur Durchsetzung von Landrechten und nachhaltigen Lebensgrundlagen ist. Der Artikel zeigt, dass fortgesetzte Landnutzung und ubliche Regierungsfuhrung die lokale Werte, Kenntnisse, Praktiken und Traditionen sowie Landbewirtschaftung Techniken charakterisieren und schliest mit einer Diskussion uber die Politik der Anerkennung dieser Elemente des indigenen Kulturerbes.

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 score0.043
Threshold uncertainty score0.316

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0160.006
Scholarly communication0.0080.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.000

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 head0.060
GPT teacher head0.280
Teacher spread0.220 · 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
Published2021
Admission routes1
Has abstractyes

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