Riverine Complexity: Islandness, socio-spatial perceptions and modification—a case study of the lower Richmond River (Eastern Australia)
Bibliographic record
Abstract
In its initial incarnation, island studies regarded islands as highly distinct entities that justified a relatively closed discipline. This orientation was first widened by address to issues such as the linkage of (pre-existent) islands to adjacent areas and, over the last decade, has been further modified by consideration of island-like areas. The latter has led to an increasing acknowledgement that, in some contexts, at least, islands form components of complex aquatic and terrestrial systems in which islandness is a less distinctive attribute than in the case of archetypal (usually marine) islands. While neglected by island studies, river systems that include islands, peninsulas and/or engineered waterfront developments are significant for problematising the distinctiveness of islands and thereby merit attention. This article sets out to map some of the complexities around islands and rivers with specific regard to the Richmond River in northern New South Wales, Australia. We profile this river since its lower reaches feature a range of natural and artificial island and island-like features, including the engineered area known as Ballina Island. We develop our study with regard to both Indigenous perceptions of the riverine space and disruptions, interventions and innovations resulting from European settlement in the region. As such, the article attempts to progress island studies’ research on riverine environments, to problematise the notion of discrete islands/islandness in such contexts and to prompt greater disciplinary reflection on the issues arising from such scrutiny.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".