On Livability, Liveability and the Limited Utility of Quality-of-Life Rankings
Bibliographic record
Abstract
When Calgary placed fifth on the Economist Intelligence Unit’s Liveability Ranking in 2012, the city’s mayor, Naheed Nenshi, quickly acknowledged how the city’s spot on the ranking proved “[Calgary has a] thriving business community, and a vibrant cultural scene that is attracting people from around the world”. Calgary’s 32nd place on the Mercer Quality of Living Index did not attract the same attention from the Mayor, or the local media. Mayors and the media alike are big fans of quality-of-life rankings whenever their cities earn a well-placed spot. But the fact that Calgary can place so highly on one ranking and so middlingly on another in the very same year is evidence of just how varied these rankings are and how misleading their interpretation can be. Made from a blend of data and feedback, and sometimes relying heavily on “good-natured, frequently late-night and jetlagged debate,” these rankings are impacted by which cities are selected, which data are used, and how the data are organized and weighted. Even amongst the rankings, agreement on what constitutes “livability” is a point of contention. Vancouver can jump from 15th place on Monocle magazine’s list, to third place on the Economist Intelligence Unit’s, and not even make the cut on PricewaterhouseCoopers’ ranking. Yet, when cities celebrate their place on these indexes, it is frequently the narcissism of small differences. In the Economist Intelligence Unit’s 2014 Liveability Ranking, there is a scant 1.8 per cent difference between top-ranked Melbourne’s overall score and that of 10th-place Auckland. In fact, nearly half the cities ranked (64 of 140) had scores above 80 per cent, meaning they present “few, if any, challenges to living standards.” The upshot, of course, is that “liveability”, as defined by The Economist, is biased toward those cities that are the least challenging for residents. That hardly qualifies one as an exceptional city, let alone the “best” of anything. Some of these rankings were created with the explicit intention of assisting businesses in assigning compensation for expatriate workers. They have quickly become something more. Lists designed for specific audiences and uses, have become a promotional tool for publicity-hungry and somewhat self-conscious cities. When tailored at a particular niche audience — grad students, for instance, or retirees — they can be useful. But the temptation to use these lists to develop public policy must at all costs be avoided. The reality is that the quality or “livability” of a city is very much a matter of personal preference. Calgary may be a less challenging place to live than San Francisco or Saigon, but whether that makes it a better place to live is a question that cannot simply be quantified by a quality-of-life ranking.
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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 | 0.069 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.021 | 0.035 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".