Classifications of environmental quality effects: The case of canadian cities
Bibliographic record
Abstract
Amenities are goods and services that make certain locations attractive for living and working. \nQuality of life on the other hand can be perceived as an expression of well-being and its importance \nis demonstrated by a number of publications that have been developed and rank quality of life \nacross cities and states based on their observable characteristics. Amenities’ assessments are \nemployed in order to produce an index to rate quality of life. It is increasingly accepted that well- \nbeing cannot be entirely based on measures of income, wealth and consumption. Other indicators \nmore qualitative (i.e. environment) should be considered. In the broader context, quality of life \nmeasures traditional economic goods such as food and accomodation but also more qualitative \nfactors such as environmental and social (i.e. fresh air, low criminality). Environmental factors \nlocated in a given place can be considered as part of the wealth of the region in which they are \nlocated. A classification of the effects of environmental quality on consumers’ utility and producers’ \ncosts that is based on housing prices and income differentials is useful because it provides \ninformation about the relative attractiveness to them of the total bundle of environmental and other \nattributes indigenous to each region. A theory is presented for this kind of analysis and \nclassifications producing a qualitative evaluation of cities. The methodology used a number of \nCanadidan cities as a case study. An amenity-productivity classification was produced and cities \nwere eventually classified as Low/High Amenity and Low/High Productivity providing useful \ninformation as to their relative attractiveness to firms and households.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".