Bibliographic record
Abstract
The problem of resourcing and staffing, or finding how much manpower is needed to meet demand, can be traced back to the times of the Roman Empire.We examine here the various means used by the Royal Canadian Mounted Police in trying to solve this problem efficiently.We also examine their latest attack in building a simulator to determine future demands on resources and we provide a solution to determine efficient staffing levels through an application of a scheduling algorithm using "rods".This algorithm is characterized as a rod-scheduling method which can be reduced to a linear program.It has been found that the previous methods used by police departments in Canada and the United States are extremely cumbersome.The methods suggested here correct this.Although the methods are very similar to those used before in other industries they haven't been applied to police work.What was previously done in policing is to optimize very simple constraints first and then try to fit the results to the needs of the user.In this work I have suggested first obtaining all legal inputs and then optimizing to obtain a final answer.The author uses this method to investigate different types of demand data.Furthermore, different integer programming techniques are investigated.This document is meant for different users.It is hoped it can be read by various police departments as well as administrators and academics.In light of this the tone of the thesis is conversational.Much of the mathematical work is in sections three to seven.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".