Demographic carrying capacity model: A tool for decision-making in Rapa Nui
Bibliographic record
Abstract
The increase of population in Rapa Nui (Easter Island) has fueled concerns within the community, given the uncertainty of its impacts. These concerns have driven a socio-political process that triggered the enactment of Law 21,070, which regulates the access and permanence of visitors in the territory as a way to cushion the pressure on different environmental, social, and infrastructure components that affect the local quality of life. However, for its application, this law requires technical foundations that allow restrictions to be applied and, therefore, knowledge about the demographic capacity of the territory is also needed. To this end, a dynamic model was built, which consists of different variables that are sensitive to population growth and also can be projected into the future, thus delivering timely information for decision-making. This paper describes the socio-political context for the creation of this instrument, as well as its elaboration process and main results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".