Waiting to Hear His Voice: How the Use of an At-Home Scanner Shaped the Lives of a Central Alberta Family
Bibliographic record
Abstract
Winner of the Student Writer Award Bronze Medal, Research Essay Category (1st-2nd Year). Although we are acquainted with the home police scanner as a vigilante crime-solving tool in movies and television, for the Wareham family whose patriarch John Douglas Wareham is a member of the Royal Canadian Mounted Police (RCMP), the home scanner is used as a reassurance of safety, and a tool for planning out their daily lives. The Warehams purchased the scanner over twenty years ago and had kept it on and playing 24 hours a day, seven days a week, until the RCMP switched to an encrypted digital format for their communication in 2019. The scanner itself is the product of police shaping the broader medium of radio, originally a tool used by hobbyists that was later intended to connect and unite citizens, into a tool for organizing and deploying police officers. This research paper will focus on the ability of the Wareham family to shape the medium of the radio scanner into their daily lives, as well as the influence exerted onto them by the medium.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.017 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".