Bibliographic record
Abstract
More problematic than his avoidance of recent geographic scholarship is his treatment of indigenous, ethnographic and postcolonial perspectives on island life. Not only is much of this scholarship absent, the bits that are there are mostly derided. He slams Kamau Braithwaite and his concept of ‘tidalectics’ as ‘unpackable’ (p. 20) and also claims that Greg Dening’s approach to islands as having ‘permeable cultural boundaries’ has ‘intellectual costs’ (p. 23). In a section of the book on ‘Naming and Sovereignty’, instead of an in-depth examination of the processes of decoding and recoding that goes on in indigenous island landscapes under colonialism (as could be discussed at length if Shell chose to examine Aotearoa, Hawaii, or hundreds of other places) we are instead presented a vignette about his childhood street fights with other kids over the naming of a hometown island in Canada, as well as ruminations about what Herman Melville and Ellen Semple thought of islands in the nineteenth century. In short, if you want to know what dead Caucasians like Shakespeare, Melville, Kant, Mackinder, More, and ancient Athenians thought about islands, then this book is a good resource. If, however, you are looking for information about what islands are like today – and what they mean to the people who live on and interact with them – then you will have to look elsewhere.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".