Bibliographic record
Abstract
Islands can be attractive places to live, with strong community networks that contribute to a good quality of life. Their distinctiveness and particularity can be seen as exotic, mysterious, offering a place where you can step back in time and enjoy a slower pace of life – or so the marketers tell us. This module explores the pluses and minuses of using ‘emotional geographies’ of islandness to create a cultural fusion that utilises place, history, and culture to meet the needs of 21st-century islanders – and tourists. We will look at specific examples of how islands on opposite sides of the globe – in Atlantic Canada and Tasmania – use the island ‘brand’ to build strong resilient communities. Artists have turned islandness to their advantage and have found ways to combine lifestyle choices with making a living. The business of art that takes inspiration from the local—in this case, islands—is becoming increasingly recognised as a significant contributor to the economy as more and more people hunger for culture grounded in the exoticism of the particular—again, from islands. And, in recent years, as islands have become more accessible to the travelling public, island artists endeavour to take greater advantage of the tourism industry to make money from their art. All of these elements combine to enable artists to remain—and make art—on and about and through their islands.
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 | 0.001 |
| 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 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".