Ageing bodies, driving and change: exploring older body–driver fit in the high-tech automobile
Bibliographic record
Abstract
Today's world of automobiles, in its wondrous integration of mechanical and digital technologies, including the prospect of self-driving cars, is far removed from anything traditional manufacturers or drivers could have imagined. The embedding of advanced vehicle technology into the automobile has resulted in vehicles that are more akin to ‘hightech devices on wheels’. The term advanced vehicle technologies (AVTs) refers to sophisticated computer and electronically mediated systems that assist drivers with driving-related tasks, provide warnings to prevent a crash, and, at times, assume control over driving. As outlined in Canada's road safety strategy 2025 , AVTs, which include back-up cameras and adaptive cruise control, are considered to have the potential to improve driver safety (Canadian Council of Motor Vehicle Administrators, 2016). Unsurprisingly, in a context whereby communities are increasingly populated by older drivers, gerontologists, transportation experts and human factors specialists have all heralded AVTs as particularly relevant for older people – viewing AVTs as offering the means to compensate for age- and health-related changes that can affect driving (see, for example, Dickerson et al, 2007). Scholars have thus begun to assess whether AVTs can redress critical driving skills among older drivers. A recent literature review provided evidence to support the claim that AVTs can assist older drivers in ways that improve their behind-the-wheel performance (Eby et al, 2016). While safety and driving performance are key public concerns, driving also features strongly in everyday lives, in feeling and achieving independence, and in social mobility, as well as simply in the experience of movement in and around communities. Driving involves a somatic intimacy between the older body, person, machine, the means to meet daily needs (for example, fetching groceries and so on) and larger links with society and/or social contacts (for example, socialising). And yet, less attention is devoted to older drivers as ‘agents’ and/or to understanding the experiences of driving. Employing qualitative interviews, we focus on the experience and changing relationship between the older driver and the technological vehicle. We explore the ‘fit’ between an older driver body and a high-tech automobile from an embodied and phenomenological point of view. The first part of this chapter explains the theoretical basis of the study as we explore how phenomenological and sociological perspectives on the body contrast with the dominant human factors approach that guides scientific investigation on ‘user’ experience and driver characteristics in relation to advancements in human–machine interfaces.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".