Old age is also a time for change: trends in news intermediary preferences among internet users in Canada and Spain
Bibliographic record
Abstract
Abstract The social distancing imposed by the Covid-19 pandemic accelerated the digitalisation of societies, which also influenced habits related to the consumption and dissemination of news. In this context, older individuals are often blamed for contributing to disinformation, which is associated with the echo chambers fostered by social media. Mass media, social media and personal communication tools act as mass, social or personal intermediaries when it comes to keeping up to date with the news. This paper analyses the preferred intermediaries of older online adults (aged 60 and over) for following the news and how they change over time. We analysed two waves of an online survey-based longitudinal study conducted in Canada and Spain, before Covid-19 pandemic (2016/17), and during Covid-19 (in 2020). We found that most participants exclusively use mass intermediaries or combine mass with social and personal intermediaries to keep abreast of the news. However, only 28% of respondents inform themselves exclusively through the alleged echo chambers of social and personal intermediaries. Results also show that media ecologies evolve in different directions, and, despite the forced digitalisation driven by the pandemic, digital media usage did not always increase or evolve towards newer technologies. This paper contributes to understanding the diverse intermediaries used by older adults to obtain news and how such media ecologies can contribute to contrasting different sources of information beyond the alleged echo chambers of social media.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".