Bibliographic record
Abstract
Beyond Hostile Islands examines five of the most significant historical and thematic areas associated with the Pacific War: island combat, economic competition, internment, imprisonment, and the atomic bombing of Hiroshima and Nagasaki. Throughout, the central issues pivot around the question of how or whether at all New Zealand fiction writing differs from that of the United States. Can a sense of islandness, the “tyranny of distance,” Māori cultural heritage, or the political legacies of the nuclear-free movement provide grounds for distinctive authorial insights? As an opening gambit, Beyond Hostile Islands puts forward the term “ideological coproduction” to describe how a territorially and demographically smaller national culture may accede to the essentials of a given ideology while differing in ways that reflect the historical and provincial dimensions that are important to it. Appropriately, the literary texts under examination are set in a wide variety of locales, including Bougainville, Kwajalein Atoll, New Mexico, Ontario, and the Solomon Islands, among others. The book concludes with a deliberately open-ended pose, in the full expectation that literary writing on the Pacific War will grow in range and richness, aided in turn by the growth of Pacific Studies as a research area.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".