Bibliographic record
Abstract
This chapter is a companion to the previous one and extends many of its themes, but this time in relation to territorial frontiers. ‘Frontiers’, as understand here, are simultaneously geographically peripheral to existing centres of political and economic power, objects of outward expansion and colonisation, and home to both abundant resources and local populations who are routinely marginalised, excluded and sometimes expelled in the name of development. The chapter explores such frontier dynamics in relation to ‘water frontiers’ within Sudan, the Palestinian territories, the Lake Chad region and north-eastern Syria. It shows that frontiers are sites of extreme levels and forms of appropriation, inequality, degradation, conflict and insecurity, as well as resilience and resistance, both in general and in relation to water specifically. And the chapter closes, in line with previous ones, by turning to climate change, noting that frontiers are widely misunderstood within climate crisis discourse – and by reflecting on how they are actually likely to fare as the planet warms.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.143 | 0.030 |
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".