Digital Society: Intergenerational conflict between myth and reality
Bibliographic record
Abstract
Intergenerational conflict is an inevitable phenomenon, present in all societies and times, based on differences in mentality, values and life experiences. Over time, this conflict has often been interpreted through myths and stereotypes that exaggerate the contrast between the young and the old. On the one hand, young people are often seen as rebellious, non-conformist, disrespectful of traditions and too concerned with new technologies. On the other hand, older generations are often characterized as rigid, difficult to persuade to change, and unable to adapt to new social and cultural realities. However, reality is much more complex than these generalizations. Intergenerational conflict is not only an opposition between old and new, but also an image of the way in which society evolves. Each generation has its own landmarks and challenges, shaped by the historical and technological context in which it lives. In many cases, generational differences are not necessarily an obstacle, but rather an opportunity for mutual learning and adaptation. This is why it is important to analyse both the myths that fuel these tensions and the realities that engender them, trying to identify solutions for generations to better understand each other and collaborate. This paper aims to explore, with the help of a survey, the manifestations of “intergenerational conflict”, balancing both traditional perspectives and the changes that shape relations between young and elderly people in contemporary society, a society strongly influenced by digital communication.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".