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Cultural-Land Bond: A Multifaceted Exploration of First Nations’ Historical Trajectory, Present Realities, and Sustainable Futures

2024· article· en· W4390814179 on OpenAlexaboutno aff
Yiyin Qu

Bibliographic record

VenueLecture Notes in Education Psychology and Public Media · 2024
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousSustainabilityEnvironmental ethicsPoliticsLegislaturePolitical scienceLegislationSociologyPolitical economyLawEcology

Abstract

fetched live from OpenAlex

This article explores the intricate and enduring connection between First Nations communities in Canada and their ancestral lands. The central research question guiding this investigation is: How do these multifaceted factors intersect to shape the lasting cultural-land bond? Utilizing a comprehensive literature review methodology, encompassing historical records, scholarly works, and indigenous perspectives, this study synthesizes information to provide a nuanced understanding of the complex dynamics at play. The key argument put forth is that the historical trajectory, colonial-era policies, Indigenous-colonial treaties, present-day social structures, economic considerations, environmental challenges, familial traditions, and core cultural values collectively contribute to the vitality of the cultural-land connection. The academic impact of this research lies in its revelation of patterns and common themes, offering crucial insights for policy adjustments. The article advocates for comprehensive legislation that respects Indigenous rights, encourages community-led initiatives, and promotes environmental sustainability. The implication of this work is a call to action, urging continued research, legislative support, and community engagement to ensure the preservation of this enduring connection for the benefit of current and future generations.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.758
Threshold uncertainty score0.486

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.009
Science and technology studies0.0140.021
Scholarly communication0.0120.007
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.395
Teacher spread0.345 · 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
Published2024
Admission routes1
Has abstractyes

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