Bibliographic record
Abstract
Purpose This paper aims to relate early history of housing conceptualizations and market analysis in the Anglosphere (Britain, the USA, Canada, Australia and New Zealand). Historians are ignorant of them but clear market analyses had early beginnings in every urban society for developing and accommodating growing populations. Design/methodology/approach Historiography. Findings Aspects of market analysis, especially appraisal and rudimentary approaches to the housing market in the Anglosphere, can be traced back to ancient Rome, housing market conceptualizations to Dr Nicholas Barbon and seventeenth-century London’s first population and housing boom and market analysis techniques in the USA at its founding, when Charles-Maurice de Talleyrand Perigor was the first to refine them and write them up in 1794-1796. The US next made major advances in the 1930s. The overall trend has been from inferred analyses to fundamental (derived) analyses, emphasizing “quantifiable data.” Practical implications This paper elicits researcher’s professional awareness that each nation has an implicit history of its early development practices and techniques. Originality/value The time frame of most housing market analysts is the recent past, the present and the future. But how enduring are their concerns? Do operational values in a housing market reflect historical epochs, or are there some universalities? Furthermore, most urban historians are ignorant of urban market dynamics. It does not occur to them that some of the dynamics that analysts attempt to capture today might always have been inherent in the urban built environment, regardless of era or urbanized part of the globe under consideration.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".