Bibliographic record
Abstract
The Federal Railroad Administration’s (FRA) Office of Railroad Policy and Development believes that, in addition to process and technology innovations, human factors-based solutions can make a significant contribution to improving safety in the railroad industry. This belief led FRA to implement the Confidential Close-Call Reporting System (C3RS), which includes voluntary confidential reporting of near-miss events to a neutral third party; root-cause- problem solving by a Peer Review Team (PRT) composed of labor, management, and FRA representatives; implementation of corrective actions; tracking of the results of change; and reporting of the results of change to employees. Demonstration pilot projects are underway at Union Pacific Railroad (UP), Canadian Pacific Railway (CP), New Jersey Transit (NJT), and Amtrak. C3RS also embodies the risk reduction and system safety principles espoused by FRA's Office of Railroad Safety that supplement conventional regulatory oversight and enforcement activities. FRA is also sponsoring a rigorous evaluation of three important aspects of C3RS functioning: (1) What conditions are necessary to implement C3RS successfully? (2) What is the impact of C3RS on safety and safety culture? (3) What factors help to sustain C3RS over time? This report is published to provide the public and government and industry decision makers with the evaluation’s findings. The findings here cover the midterm analysis of C3RS at one demonstration site (Site “A”) and are based on data collected and analyzed using five data sources: interviews with workers, managers, and other stakeholders; railroad newsletters; corporate safety data; corrective action data; and redacted Multiple Cause Incident Analysis (MCIA) results from a third party.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".