When Value is Created but There is No Record: An Eyewitness Account of Public Value Creation in a Student Resource Officer Program
Bibliographic record
Abstract
Conceptualizing and measuring public value presents many challenges for researchers, particularly in those cases where the program being evaluated has visible expenses and hard to quantify outputs or outcomes. Program stakeholders might not even recognize the public value (PV) they are creating. PV theory lacks empirical research and faces the possibility of theoretical stagnation. Efforts at measuring PV have not yielded consensus. This study employs ethnography to investigate how an external observer sees the PV being created by the school resource officer (SRO) program. This paper reports the key findings from a case study into the value delivered by a controversial public program, a co-operative initiative between a regional police service and the region’s educational system. This evaluation comprised ten ride alongs with SRO program officers over a five-month period. Forty-one stories of PV being created were observed, which were then grouped into six themes/focused codes. In addition to the program’s desired outcome of building safer schools, this paper identifies five other types of PV creation. These outcomes represent important contributions to the public and allow for the potential comparison of how tax dollars are spent, informing future allocation of funds and potentially saving good programs.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".