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Record W4214484640 · doi:10.5509/202295149

Feeling “Superstitious”: Affect and the Land in the Marquesas Islands

2022· article· en· W4214484640 on OpenAlexvenueno aff
Emily C. Donaldson

Bibliographic record

VenuePacific Affairs · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousSovereigntyAmbivalenceColonialismShameFeelingSociologyEnvironmental ethicsPolitical scienceSocial psychologyLawEcologyPsychologyPolitics

Abstract

fetched live from OpenAlex

For many Indigenous peoples, ancestral lands are a source of nourishment, strength, and sovereignty that counteracts colonial legacies of violence and hegemony. However, the feelings associated with place and the land can also be complicated by embodied fear and ambivalence. What happens when the remnants of colonialism feed feelings of ambivalence, shame, or fear of the land? How do these lasting emotional scars on Indigenous minds and bodies impact Native place-making, today? This paper problematizes the role of ancestral lands and affective place-making in shaping Indigenous identity, sovereignty, resource management, and sustainability. In the Marquesas Islands of French Polynesia, ancestral places are felt as much as seen, and the spirits that dwell there can be dangerous. The active concealment of these Marquesan reactions and relationships to place illustrates the blending of colonial and Indigenous histories and values in ambivalent, affective experiences on the land. Thus, even as islanders work to revitalize their traditional culture and build a sustainable future based on ancestral places, reticence complicates local relationships to the land and the vital hopes they represent. As global sustainability efforts emphasize the conservation of lands inhabited by Indigenous communities, recognizing the conflicted, emplaced emotions and experiences of local peoples will be a key part of understanding such areas and how to preserve them.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.254
Teacher spread0.240 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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