Bibliographic record
Abstract
INVENTING 'EASTER ISLAND'Easter Island, or Rapa Nui, as it is known to its inhabitants, is located in the Pacific Ocean, 3600 kilometres west of South America.Annexed by Chile in 1888, the island was first visited by Europeans in 1722, and has since attracted widespread interest owing to its intriguing statues and complex history.Inventing 'Easter Island' examines narrative strategies and visual conventions in the discursive construction of 'Easter Island' as distinct from the native conception of 'Rapa Nui.' Beginning with a look at the geographic imaginary that pervaded the eighteenth century -a period of rapid imperial expansion -Beverley Haun discusses the forces that shaped the European version of island culture.She then goes on to consider the various representations of that culture, from the sketches and journals of early explorers to more recent texts and images, including those found in comic books and numerous forms of kitsch.Throughout, Easter Island is used as a case study of the impact of imperialism on the outsider's view of a culture.The study hinges on three key investigations -an inquiry into the formation of Easter Island as a subject; an examination of how the constructed space and culture have been shaped, reshaped, and represented in discursive contexts; and an exploration of cultural memory and the effect of foreign texts and images on perceptions and attitudes in regard to the island and its people.Richly illustrated and engagingly written, this fascinating and provocative study will appeal to cultural theorists, anthropologists, educators, and anyone interested in the history of the South Pacific.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.868 | 0.703 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".