Bibliographic record
Abstract
Abstract The history of Rapa Nui has often been told as a cautionary tale meant to reflect on the destructive tendencies of our own civilization, but it looks quite different when told from a Rapanui perspective. This interdisciplinary study uses new source materials to explore an extraordinary story of Native resilience: seldom-used oral histories from Rapanui elders from the 1910s, a Rapanui-language account of its original settlement, and a newly reconstructed Rapanui Chronology of Years, as well as the latest physical data of environmental change on the island, reconstructions of the ancient night sky, and an innovative model of the historical productivity of traditional crops. They reveal the exact stars and winds the ancient Rapanui followed to locate this island originally; their embrace of new crop varieties, animals, and cultivation methods to ensure their subsistence; and their vulnerability to severe droughts and climate change. Rapanui resilience experienced its greatest test during the 1860s and 1870s when interethnic conflict, a punishing La Niña drought, introduced diseases, plantation enslavement, Catholic missionization, and colonization by outsiders struck the island in waves. But Rapanui elders remembered these events as much for their tenacious resistance and resilience to these challenges as for their tragic loss.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".