Social media and senior citizen advocacy: an inclusive tool to resist ageism?
Bibliographic record
Abstract
With population aging, interest groups demand that governments act to prevent a perceived financial crisis. Senior citizens remain frustrated in their efforts to influence the response of policy-makers. In an effort to strengthen their voice, one group of senior citizens, engaged in a participatory action research project, questioned how online social media could be used in their advocacy efforts. This query led to an examination of the literature with the primary objective of determining what is known about the use of social media by senior citizens for the purposes of social advocacy. The outcomes of the review revealed that very few studies specifically examined this question. Senior citizen online roles were depicted as consumers of health information or socializers with family and friends. Ageist assumptions informed the design of computer hardware, online formats and norms for social engagement. Senior citizens have concerns about the trustworthiness of social networking sites and while social media can exclude senior citizens from public debate, the authors conclude that the pressing issue is to focus on age-friendly design and supports. With these in place, social media can provide a venue for senior citizens to challenge ageism and influence public policy discourses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".