Older audiences in the digital media environment: A cross-national longitudinal study. Wave 2 Report v1.0
Bibliographic record
Abstract
The Ageing + Communication + Technologies (ACT) cross-national longitudinal study explores processes of displacement of traditional dominant media by innovative communication practices within the older audience of new media. Replicating Nimrod’s (2017) study of older audiences, data is collected on a biannual basis. The first wave of data collection was based on surveys with Internet users aged 60 and up and took place in November 2016. Quotas were instituted to ensure that each sample is representative of the country’s older online population. With varying expected dropout rates, the original samples were planned to have a final panel that will comprise about 500 participants per country. For this reason, sample sizes in the first wave were not equal and ranged between 715 (Denmark) and 3,538 (Canada). The overall sample size consisted of 10,527 Internet users aged 60 and over. For a full report of the first wave please see Loos, Nimrod & Fernández-Ardèvol (2018). The present report relates to the second wave of data collection, which was held in November 2018. In this second wave, we returned to participants from six countries that took part in the first wave (Austria, Canada, Israel, Netherlands, Romania, and Spain). In addition, we interviewed older Internet users from Finland—a country that was not included in the first wave. Unfortunately, Denmark, which was included in the first wave, was not part of the second wave. Data were collected by the same commercial firms that collected the data in the first wave. With the exception of Romania, where the survey was conducted via telephone due to a low rate of Internet users among the older population, all firms applied an online survey. In the second wave, we tried to contact all the participants from the first wave. Study participants were reached out by the firms and were sent several reminders during the data collection period. Overall, 8,447 people who participated in the first survey were contacted. Repeated response rates ranged between 61% and 86% with the highest in the Netherlands. The total number of repeated participants included in the final dataset was 6,225. In addition, Spain recruited 172 new participants and Finland added 1,520 participants, leading to an overall sample size of 7,940 Internet users aged 62 and over in the second wave.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".