Bibliographic record
Abstract
Until the mid-1990s, much of Ontario's municipal asset management was closely linked to the conditional funding grants administered centrally by the provincial government. In the years since, there has been a succession of short-term funding programs that have required municipalities to commission expensive studies in support of their grant submissions, or to link their capital investment in one category of infrastructure with infrastructure assets of lesser priority in order to qualify for partial funding. As a result, local investment in the construction and maintenance of municipal capital assets has been both sporadic and by and large, woefully underfunded since 1996. The result is a province-wide patchwork quilt of infrastructure that is in an undeniable state of decline. In August 2012, the government of Ontario announced a new precondition for municipalities seeking funding support for capital works. They must now show sound management of their assets through the preparation of an asset management plan, and demonstrate how the proposed infrastructure project will support their overall asset management plan. The deadline for submission of these municipal asset management plans to the province is December 2013. The Ontario Good Roads Association (OGRA) created the Academy for Municipal Asset Management accreditation program to develop the skills necessary for our member municipalities to meet these new challenges in the management of their tangible capital assets. The stated goal of the program is to develop the skills and knowledge required to manage the financial, capital, and operations needs of public infrastructure assets. For the covering abstract of this conference see ITRD record number 201310RT334E.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.258 | 0.140 |
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".