Bibliographic record
Abstract
Over the past 30 years Calgary has doubled in size, from a population of 640,645 in 1985 to 1,230,915 in 2015. During that time the City has had five different mayors, hosted the Winter Olympics, and expanded the C-Train from 25 platforms to 45. Calgary’s Metropolitan Area has grown too, with Airdrie, Chestermere, Okotoks and Cochrane growing into full-fledged cities, ripe with inter-urban commuters.* And with changes to provincial legislation in the mid-’90s, rural Rocky View County and the Municipal District of Foothills are now real competitors for residential, commercial and industrial development that in the past would have been considered urban. In this metropolitan system, where people live, their household structure, and their place of work informs the services they need to conduct their daily lives, and directly impacts the spatial character of the City and the broader region. In sum, Metropolitan Calgary is increasingly complex. Calgary and the broader metropolitan area will continue to grow, even with the current economic slowdown. Frictions within Calgary, between the various municipalities in the metropolitan area, and the priorities of other local authorities (such as the School Boards and Alberta Health Services) will continue to impact the agendas of local politicians and their ability to answer to the needs of their residents. How resources – whether it is hard infrastructure, affordable housing, classrooms, or hospital beds – are allocated over space and how these resources are funded, directly impacts these relationships. This technical paper provides my perspective as an urban economist on the efficient allocation of resources within a metropolitan system in general, with reference to Calgary where appropriate, and serves as a companion to the previously released “Reflections on Calgary’s Spatial Structure: An Urban Economists Critique of Municipal Planning in Calgary.” It is hoped that the concepts reviewed herein effectively expand upon and supplement the discussion in the former paper. The urban economic perspective adopted in this paper is itself not inconsistent with the public economics perspective but it pays more attention to space, and metropolitan transportation and land use policy, and less to tax policy and intergovernmental fiscal arrangements. I will explore the following sources of inefficiency within a metropolitan system: 1) local public goods, 2) externalities, and 3) economies of scale, starting with a short background on classic market failure and second-best policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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".