Implementing New Technologies to Reduce Operating Costs in the City of Palo Alto: An Exploratory Study
Bibliographic record
Abstract
The most recent recession has left government agencies in California looking for creative ways to maximize their budgets. There are several fiscal challenges that have contributed greatly to this significant downturn. Proposition 13 in the late 1970's limited local government's ability to increase property taxes, State takeaway of property taxes, the escalating cost of pension and health care benefits and growing need to fix aging infrastructure. It is not uncommon to hear about public agencies cutting resources and staff to stay within budget leaving executive managers the task of recreating their business processes to maintain similar levels of services that their communities are accustomed to receiving.\nThe City of Palo Alto is no exception; it is difficult to continue providing similar levels of service when revenues are not keeping up with expenditures. This paper will discuss potential opportunities for the City of Palo Alto to realize efficiencies in utilities payment processing by comparing other public agencies including their use of technological tools and the contracting out of payment processing services. The process will include conducting a survey of other public sector agencies to determine if they process payments in-house or outsource, measuring the number of utilities and volume processed, staffing levels, and identifying efficiencies experienced. The research will also include interviews with Revenue Collections staff to obtain their observations and ideas on adding efficiencies or tools to assist in the process. The research will include a financial analysis including reviewing prior year program expenses and estimated future costs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".