Article Analysis of Newspaper Coverage of Active Aging through the Lens of the 2002 World Health Organization Active Ageing Report: A Policy Framework and the 2010 Toronto Charter for Physical Activity: A Global Call for Action
Bibliographic record
Abstract
Abstract: As populations continue to grow older, efforts to support the process of aging well are important goals. Various synonyms are used to cover aging well, such as active aging. The World Health Organization published in 2002 the report Active Ageing: A Policy Framework that according to the call for papers, has brought active ageing to the forefront of international public health awareness. The 2010 Toronto Charter for Physical Activity: A Global Call for Action was singled out in the call for papers as a key document promoting physical activity one goal of the 2002 WHO active aging policy framework. Media are to report to the public topics of importance to them. We investigated the newspaper coverage of aging well and synonymous terms such as active aging through the lens of the 2002 WHO active aging policy framework and the 2010 Toronto Charter for Physical Activity. As sources we used the following newspapers: China Daily, The Star (Malaysia), two UK newspapers (The Guardian, The Times), a database of 300 Canadian newspapers (Canadian Newsstand) and a US newspaper (The New York Times). The study
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".