The Relationship between Population Dynamics and Urban Hierarchy
Bibliographic record
Abstract
Spatial planners often make “comprehensive” decisions on the location of public service facilities by using the concept of urban hierarchy: population centers at the upper level of the hierarchy (typically large cities) get the highest level facilities, such as specialized hospitals and universities, while the centers at the lower levels of hierarchy get lower-level facilities. Intuitively, this suggests that there should be a link between urban hierarchy designations and population dynamics in future periods. This link must be taken into account in planning decisions, as it suggests that today’s decisions affect tomorrow’s demand for services. Indeed, this link was assumed in a previously published planning model. Yet, no direct evidence of such a link appears in the literature. The primary goal of this article is to fill this gap by using the census data for Portugal for the period 1991–2001 and the changes in the urban hierarchy that were implemented during 1980s and early 1990s. While our results support the link between urban hierarchy designations and population dynamics that has been assumed in previously published work, the mechanism describing this link appears to be somewhat different from the one postulated previously. Several extensions and directions for future work are also discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".