Older audiences in the digital media environment: A cross-national longitudinal study: Wave 1 Report 1.0.
Bibliographic record
Abstract
Involving teams from seven countries (Austria, Canada, Denmark, Israel, Netherlands, Romania, and Spain) this Ageing + Communication + Technologies (ACT) project offers a unique opportunity to explore possible 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 will be collected on a biannual basis over a five-year period (overall three waves).The first wave was based on surveys with Internet users aged 60 and up, to whom we will return in the following waves. Data was collected by local commercial firms. 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. Most data was collected between November and December 2016, with the exception of Canada, where the data were collected between June and July 2017.With varying expected dropout rates, the samples were planned to have a final panel that will comprise about 500 participantsper 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. To reach this sample size, the firms contacted a total of 33,035 individuals. Response rates ranged between 8.9% and 64.6% according to percentage of older people who use the Internet in the country and data collection method. The response rate was at its lowest in Romania, where there was a need to screen out older adults who do not use the Internet, and at its highest in the Netherlands. Reference Nimrod, G. (2017). Older audiences in the digital media environment. Information, Communication & Society, 20, 233-249.
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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 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".