Bibliographic record
Abstract
Commuting is regular travelling between one?s place of residence and the place of work. A lot of us surely remember our grandmothers and grandfathers talking keenly how they used to go to work to a farm, weaving factory, distillery and further facilities. It used to be usual that people had jobs at distances they could walk. On the other hand nowadays it is quite normal that some people have their places of work tens of kilometres from their homes and they have to commute by various means of transport, get up early in the morning and return late in the evening. Technical evolution has made transport faster but it also brought longer distances to travel to work. Commuting is particularly necessary at places with imbalance between supply of and demand for jobs. About one third of inhabitants commute in the Czech Republic and the travel costs represent approximately ten per cent of family budgets. People that spend more time travelling to work are wrong tempered, they are tired and stressed by commuting and occupational accidents occur as a subsequence of these factors. The aim of the Thesis ??Commuting time and workplace accidents?q was to discover what the average commuting time was and whether commuting affected the health condition of a human. Another aim was to find out how frequent workplace accidents were and how consistent employees were in reporting workplace accidents. 5 hypotheses were set to reach the aim. A quantitative method of questionnaire survey was applied on the research; the data were collected from commuting employees of a health facility, a school and a manufacturing company and compared. The data analyses did not prove that commuting caused health problems, so the hypothesis No. 1 was refuted. The second hypothesis suggesting that people from villages spend more time commuting than those living in towns was confirmed. Another hypothesis that each employee has suffered from a workplace accident was on the other hand refuted. The fourth hypothesis suggesting that fatigue, bad concentration, insomnia as a subsequence of commutation cause workplace accidents was also refuted. The last hypothesis suggesting that employees often do not inform their employers on their workplace accidents was also refuted. The Bachelor Thesis may improve knowledge of the time spent by commuting to work and subsequent occurrence of workplace accidents. The manual designed proposes preventive measures against workplace accidents and brings information in this field.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".