Bibliographic record
Abstract
Because of rising sea levels from global warming, the entire population of the small, low-lying central Pacific island state of Kiribati will need to relocate to a distant land. In Kiribati President Anote Tong’s words, “We need to begin [the migration process] now ... because [we] will either be dead or drown.” Climate change seriously impacts 325 million people, kills 300,000 people, and costs the world $125 billion every year. By the year 2100, global average sea levels may rise up to 1.9 meters, wiping out low-lying island nations, making large parts of Bangladesh uninhabitable, and increasing the chance that major coastal cities like New York will flood. Forty-three small island countries are particularly vulnerable to rising sea-levels, and some, like Kiribati, may end up entirely underwater. Kiribati, which sits only 6.5 feet above sea level on average, is particularly vulnerable to wholenation displacement, as rising sea levels would render most of it uninhabitable. Because fewer than a quarter of residents have jobs, most I-Kiribati (Kiribati citizens) depend on employed relatives and foreign aid.6 Kiribati’s 95,000 inhabitants suffer from significant overcrowding, low incomes, poor sanitation, and severe pollution. As sea levels continue to rise, Kiribati will run out of fresh drinking water and become uninhabitable long before the islands are submerged. The country will eventually face widespread population displacement and de facto statelessness, and Kiribati may no longer have a permanent population. Other countries should help I-Kiribati transition to life in a new nation-state by giving these displaced people an opportunity to pursue an education and acquire work skills and by according them the same rights as permanent residents in that nation. The international community should aid Kiribati in tackling the effects of climate change by providing more affordable loans and grants to Kiribati; coordinating humanitarian and emergency relief efforts in times of displacement; and helping I-Kiribati relocate to ensure that their human rights are protected.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".