Bibliographic record
Abstract
Dagron (2001) argues that many development projects of the past failed to see the importance of communication to their success and, in fact, neglected long-term dialogues with local stakeholders of development initiatives. Others, he asserts, have confused communication programs for propaganda, instead of the interactive dialogue they should be, and have focused their attention on mass media campaigns in urban settings, at the exclusion of rural populations. As a field of development communication, entertainment education (E-E) has struggled through its own foggy beginnings to establish itself as a relevant and valid form of communication for social change in both rural and urban communities. Many will surely associate their own exposure to apparent attempts at education–entertainment programming with emotionally void and culturally inappropriate nature documentaries on public television channels. Others may bring to mind the barrage of public service announcements that have infiltrated our homes under the guise of entertainment. The book Entertainment-Education and Social Change (2004) is an excellent new resource on this ever-changing field and will appeal to both new and well-versed communication scholars. With its thorough focus on history, research, and practice, it could well in fact be considered a handbook for the field of E-E communication. Through a varied collection of articles and authors, the book makes it clear that the many different types of E-E initiatives taking place around the world are only finding success when the following are considered: thorough and participatory formative research, solid and respectful collaborations between media and research professionals, careful balances between entertainment and education value, and an understanding of relevant scholarly theory. Effective E-E campaigns are not randomly thrown together but are instead carefully premeditated and cooperative programs with a very focused and important purpose in mind.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.461 | 0.448 |
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".