Bibliographic record
Abstract
After years of commissions and task forces, new City of Toronto was created on January 1, 1998, amalgamating regional government of Metropolitan Toronto and six local area municipalities. The Province of Ontario's publicly stated goals for amalgamation of six local governments and regional government of Toronto into a single city included achieving cost reductions through eliminating duplication, streamlining operations, and improving efficiency in service delivery. Based on reports from city's Finance Department, city's 3-year amalgamation savings target of $150 million appears to be on track. In addition to massive restructuring of staff and services, city staff have had to contend with three other activity areas while delivering regular services to citizens of Toronto: corporate initiatives/one-time events, financial/human resources information systems, and staff recruitment. The transformation has resulted in following changes for staff: increased work hours and stress; reduced productivity, quality, and morale; and a loss of linkage to staff in other divisions. A four-step action plan was developed by Transportation Division: focus on client services and projects/activities that will enhance delivery of these services; be aware of need for formal training/counseling to help employees cope with change; encourage staff to develop new skills required to achieve their job objectives and attain job satisfaction; and bridge credibility gap between mission statement's the best municipal government in Canada and reality of downsizing and saving dollars.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".