A LITTLE LESS TALK AND A LOT MORE ACTION: HOW CAN LAW ENFORCEMENT ENHANCE THE RECRUITMENT OF WOMEN?
Bibliographic record
Abstract
Women entered the law enforcement profession over 100 years ago, and while they now account for over 50 percent of the U.S. population, they represent a meager 12 percent of the 800,000 sworn police officers serving in the country. As law enforcement agencies struggle to find enough officers to fill staffing shortages, women remain an under-recruited resource. This thesis aims to answer the question of how law enforcement can enhance the recruitment of women. A comparative analysis approach was used to compare and contrast Australia’s and Canada’s policing, recruitment practices, and maternity benefits to those of the United States. These two allied countries were chosen for comparison as they share similar democratic frameworks to the United States yet have significantly higher percentages of women serving as police officers. Findings from the analysis suggest that the strategies used in Australia and Canada have a significant impact and could be implemented in the United States to enhance women’s recruitment. U.S. law enforcement leaders must strive to move the numerically underrepresented women from token status and work to create a profession rife with diversity and inclusion. Findings suggest that law enforcement should change the focus of recruitment from the physical strength of a candidate to problem-solving capabilities, interpersonal strengths, and communication skills.
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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".