Improving public works operations and management through establishment of nationally recognized practices
Bibliographic record
Abstract
Public works agencies are under increasing pressure to be more accountable for effective operation and management of public infrastructure and the services related thereto. In the United States and Canada, the American Public Works Association (APWA) has developed over 400 recommended management practice principles that address virtually every aspect of public works. APWA has now published the third edition of the Public Works Management Practices Manual and has developed a self-assessment methodology by which public works agencies can document their existing practices against the nationally recognized policies, practices and procedures. Across the United States and Canada, hundreds of public works agencies are engaged in evaluation of their operations and management against the recommended practices in the Management Practices Manual. Many of these agencies have also committed to becoming accredited by APWA. The self-assessment process provides a systematic approach to evaluating both management and technical aspects of providing infrastructure services necessary to support urban and rural communities. Most agencies conducting these self-assessments find that the process of systematically reviewing every policy, practice and procedure is very beneficial. Often, areas of duplication or gaps are identified and immediately corrected. Agencies frequently find that there are conflicting interpretations about an agency's official policy. Differences between printed policies and actual practice are frequently identified and can be easily rectified. Operational personnel are frequently invited to participate in redefining what an agency handles and how it handles the day-to-day issues. Some managers have likened the process to the ISO 9000 process of certifying quality assurance. The self-assessment process does provide a mechanism for continuous improvement through the accreditation process, which requires re-evaluation every three years.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".