Uninhabited, Unused, Untravelled, or Uncharted? Sparsely or Unpopulated Areas and Gridded Population Data
Bibliographic record
Abstract
Gridded data sets representing the distribution of the human population are increasingly available globally today. If most of the attention they draw is directed toward inhabited areas, in this article, the authors turns to very sparsely or unpopulated areas, which allow for a renewed exploration of their reality and a reflection on the essence of ”unpopulatedness.” To do so, the author reflects on the multiplication of gridded population data sets and the methods used to produce them, using unpopulated areas as a revealing factor of the limits of algorithms. In the second part, different geographical concepts related to uninhabited areas are explored to show that their reality is not clear-cut and more difficult to grasp than data sets based on population density indicate. Very sparsely populated or unpopulated areas include a vast array of regions and situations, from areas not permanently settled but heavily used and travelled to areas that are very infrequently visited. Their levels of transformation by human activities, frequentation, or accessibility differ wildly, so it is impossible to consider them as a unique or coherent category. In conclusion, the author calls for going beyond the “nobody lives there” vision, popular on the internet, by understanding better how population gridded data sets are constructed and by complementing them with new quantitative and qualitative data, which could help distinguish between several nuances of occupation in space and time.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".