To: Executive Committee From: Deputy City Manager and Chief Financial Officer Wards: All Wards Reference
Bibliographic record
Abstract
At its meeting in October 2005, Council adopted a set of comprehensive incentives and initiatives intended to enhance the City’s competitiveness over the long term. The core fairness principles and the business cost competitiveness initiatives contained in the Plan headed “Enhancing Toronto’s Business Climate – It’s Everybody’s Business ” was intended to help to level the playing field with the surrounding municipalities and make Toronto’s businesses more competitive globally. The recommendations were developed through extensive consultation with all stakeholders, together with extensive research and analysis by staff. Taken collectively as a package they will create the conditions to help maintain and expand the City’s property assessment base, with a net positive impact on the City over the long term. Many of these initiatives, which are within the City’s sphere of responsibility, have commenced. One of the most significant initiatives is the measures that City Council adopted in October 2005 to reduce Toronto’s commercial and industrial tax ratios. Other initiatives still require Provincial regulatory and legislative changes and have not been implemented. This report provides an update on the status of the initiatives adopted by Council under the City’s “Enhancing Toronto’s Business Climate – It’s Everybody’s Business ” plan, and makes recommendations necessary to implement the remaining initiatives.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.408 | 0.281 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".