What Can District Migration Rates Tell Us about London’s Functional Urban Area?
Bibliographic record
Abstract
In the early 1990s, Anthony Fielding coined the term ‘escalator region’ to describe how London and the South East attracted those with greater human capital by offering them superior career prospects and enhanced returns in the housing markets. When delineating a housing or labour market area, it is not uncommon to require high levels of migration and commuting within the market area relative to those that cross the area’s boundaries. Net migration flows to and from this escalator region change depending on the age range one examines, making migration across boundaries relatively high. It is proposed that focusing on age ranges that reflect younger adults would capture the extent of the market. In particular, the birth of a first child is likely to trigger migration, but that movement is constrained to be within the boundary of the market area. The decision to buy a dwelling would be made around the time of this event. This paper delineates market areas using spatial autocorrelation. This has the advantage of using a statistical criterion rather than a containment value. Broadly similar areas in the Greater South East are revealed using relative housing affordability measures, the movement of infants and the migration of 20- to 24-year-olds. It is argued that the time-varying patterns of migration of 30- to 39-year-olds is reflective of a change in housing affordability, forcing more households to migrate with children whilst renting.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".