The Impact of the Regulatory Environment on the Ease of Doing Business: How Calgary Measures Up
Bibliographic record
Abstract
Municipal governments have a key role in cultivating an environment where businesses can thrive, and are responsible for shaping the local regulatory environment in which that businesses operate in. The regulatory environment includes taxes, laws, and other regulations that governing businesses’ activities in starting and operating a business. The regulatory environment of a municipality influences how competitive the city is for businesses. The impetus for this research is to provide Calgary’s economic development agency, Calgary Economic Development, with an overview of Calgary’s business competitiveness. This report assess the competitiveness of Calgary’s regulatory environment and answers the question: how does Calgary’s regulatory environment compare to other Canadian municipalities? The ease of doing business (defined as the ease of starting and operating a business) is one consideration for headquarter and operation location choice. Businesses can choose to leave a municipality or not enter at all when the cost of starting and operating a business is high. In the wake of several businesses leaving Calgary, it is important to assess the impact of Calgary’s regulatory environment on the ease of doing business. To answer the research question, this report quantitatively assesses Calgary’s performance across seven indicators and associated metrics from the perspective of small and medium enterprises (SMEs). Calgary’s performance is compared to other major Canadian municipalities (Edmonton, Halifax, London, Montreal, Regina, Toronto, Vancouver, and Winnipeg). Rather than present an overall score for each municipality, the municipalities’ performance on each metric is directly compared to identify areas where Calgary could improve.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".