Automation and digitalisation on the transport workforce: How can the shock be prevented?
Bibliographic record
Abstract
The transportation industry is experiencing a significant transformation due to the increasing adoption of automation and digitalization. As a result, professionals in the industry are increasingly concerned about how these changes will impact the workforce, and they are seeking effective strategies to address any potential challenges. This paper presents the findings of a study that sought to identify barriers and opportunities related to the impact of automation and digitalization on the transportation industry workforce. The study involved a collective intelligence and consensus-building process through structured discussions among stakeholders and partners/experts during a sequence of thematic area group convocations and focus groups, as part of the WE-TRANSFORM project. Over 70 suggestions were recorded during 25 meetings, which were then transcribed and reported through rapporteurs of thematic groups and focus groups run within thematic areas. The process included a two-round Delphi-type of survey to formulate a narrower list of the most significant actions. The study's results highlight the importance of soft skill development, such as communication, collaboration, and problem-solving, in the face of automation and digitalization in the transport industry. The findings suggest that a more significant focus on developing these skills can help address potential challenges.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".