Bibliographic record
Abstract
This paper will consider the impact of communications technologies on global peace and non violence by examining how these technologies are addressed in public policy on culture, education, health and safety. Discussion will focus on how they are employed in advertising industries to target children; ways in which special effects in the production of films, television programs, video and computer games fuel the use of violence as a form of entertainment and how this is resulting in increasing evidence of collective desensitization in communities and schools throughout North America.Manifestation of these trends is at odds with educational goals that discourage the use of violence as a form of conflict resolution, materialistic, consumer driven value systems, and unhealthy eating habits. It will be demonstrated that unimpeded proliferation of popular culture as entertainment for profit driven purposes, laced with themes of sex and violence because they sell well on a global market and translate easily into any language, will have to change, if we are to encourage transformation and sustainable development on either a local, national or global basis.Massive export of popular culture commodities by the U.S., long the world's leader in this context, has more than quadrupled in the past two decades with content increasingly coarse and violent. This is having an international as well as local and national impact. The ingrained belief that what is good for show business is good for America's image is not only gravely eroding the country's reputation around the world, but accelerating a culture of violence that is, from an educational, health and environmental perspective, at odds with efforts for a paradigm shift that will move us forward, as a species, towards social justice and a green Earth.
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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.054 | 0.006 |
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".