Making sense of safety: Figure 1
Bibliographic record
Abstract
Beyond injury prevention The concept of “safety” can have many different meanings. The Concise Oxford Dictionary defines it as “freedom from danger and risks”, while the Merriam-Webster Dictionary describes safety as “the condition of being safe from undergoing or causing hurt, injury, or loss”. According to etymologist Douglas Harper, the word safe first came into use in the English language around 1280, derived from the Old French sauf , which in turn stemmed from the Latin salvus , meaning “uninjured, healthy, safe”. The Latin word is related to the concepts of salus (“good health”), saluber (“healthful”), and solidus (“solid”), all derived from the Proto-Indo-European base word solwos , meaning “whole”.1 Thus, at its root, the concept of safety revolves around wholeness and health. Injury prevention researchers have defined safety as “a state or situation characterised by adequate control of physical, material, or moral threats”, which “contributes to a perception of being sheltered from danger” (Andersson and Svanstrom, as quoted in Welander et al , page 122). Safety is commonly viewed through the lens of specific injury domains: for some researchers in the injury prevention field, safety has come to mean the prevention of crime and violence; for others, a reduction in motor vehicle deaths or a feeling of being out of danger rather than being in a positive state of human growth and development.3 Due to the multitude of views on the definition of safety, a collaborative effort was launched in 1996 by two World Health Organisation (WHO) Collaborating Centers on Safety Promotion and Injury Prevention, sponsored by the Ministry of Health, Quebec, Canada, and Karolinska Institute, Stockholm, Sweden, to develop international consensus on the conceptual and operational aspects of safety and safety promotion.2 A document was published in 1998 entitled Safety and Safety Promotion: Conceptual …
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.095 | 0.042 |
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".