Issues in the selection of fall prevention and arrest equipment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The research objectives are to investigate current methods of fall protection, identify issues in their selection and use, and produce guidance on best practice for designers and constructors. Design/methodology/approach A steering group with both health and safety and production experience directed a variety of data collection methods: interviews with industry specialists to assist in identifying the significant issues in fall protection and selecting fall protection systems; study of published research, legislation, codes of practice, and system technical data; focus groups to investigate both generic and system‐specific issues; and visits to manufacturers, suppliers, contractors' offices and sites, to observe and discuss systems in development, planning, erection and operation. Findings This paper deals with all the general issues in equipment selection: a hierarchy of selection; legislative guidance; interaction with the structure; impact on site operations; rescue of fallers; issues specific to maintenance and refurbishment; and costs arising from equipment selection. Originality/value The paper provides a summary of the most important issues contained in the full Health & Safety Executive report of the research, the only comprehensive source of such practical guidance.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it