Privacy apathy in later life? Online surveillance perception and privacy protection among older internet users
Bibliographic record
Abstract
Background and aim: Recent research on privacy protection among internet users appears to highlight a paradox: although online surveillance is a major concern, many users make minimal efforts to safeguard their data. This phenomenon, often termed ‘privacy apathy’, has been largely unexplored in the context of later life, with limited studies examining older internet users’ perspectives on online surveillance and privacy practices. Methods: This article presents findings from an online survey conducted in November and December 2023, which examined older internet users’ (aged 60+) perceptions and practices regarding digital surveillance by commercial corporations in Austria, Canada, Israel, the Netherlands, Romania and Spain. Responses from 3,030 participants provide insights into their awareness of online surveillance and the relationship with online privacy behaviours. Results: Results reveal mixed perceptions of corporate surveillance among older internet users. Approximately half of the respondents reported no awareness of such surveillance. Notably, older participants showed lower awareness compared to younger study participants. However, those who were aware of surveillance – whether viewing it positively or negatively – were more likely to adopt privacy protection measures. Discussion: The study challenges the notion of widespread privacy apathy among older adults. Instead, it highlights that many competently engage in privacy protection practices, although not universally. Findings underscore the need for further research on digital inequalities in later life to better understand the nuances of privacy protection practices among older internet users. The discussion also underscores the need to further situate privacy apathy in later life within the broader contexts in which corporations cultivate digital resignation, ultimately producing the so-called privacy paradox as a means to legitimise large-scale data collection.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".