Advertising in the Aging Society: Setting the Stage
Bibliographic record
Abstract
The motivation for this book is grounded in several reasons. First, older people are of interest in our study because of their rapid increase around the world and specifically in Japanese society, as well as their increasing importance as a market segment (Coulmas, 2007; Kohlbacher & Herstatt, 2011). Siano and associates (2013) argue that “Understanding corporate communication strategy takes on critical importance whenever organisations are threatened by environmental changes … that lead to the redefinition of the role of the organisation in relation to its key stakeholders” (p. 151). Demographic change is such an environmental change that requires responses from corporations (Kohlbacher & Matsuno, 2012). Second, mass media in general and television in particular rank prominently among the major sources of information among older people and are tapped for purchasing and consumption decisions (Kohlbacher, Prieler, & Hagiwara, 2011a; Lumpkin & Festervand, 1988; Phillips & Sternthal, 1977; Smith, Moschis, & Moore, 1985). Third, research around the globe (including Japan) on the representation of older people in television advertising finds them to be underrepresented (Prieler, Kohlbacher, Hagiwara, & Arima, 2015; Simcock & Sudbury, 2006; Y. B. Zhang et al., 2006) and sometimes even to be portrayed negatively or stereotypically (Prieler, Kohlbacher, Hagiwara, & Arima, 2011a; Zhou & Chen, 1992). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".