Feeling “Superstitious”: Affect and the Land in the Marquesas Islands
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".