Exploring jeremiad visual rhetoric in environmental documentaries: Raising awareness about the (dis)connection between human behaviours and environmental issues
Bibliographic record
Abstract
Over the last decade there has been a significant increase in environmental communication. Environmental education is interdisciplinary, and educators of all levels should consider the merits of implementing topics of the environment into the classroom. Environmental issues are complex that are often difficult to understand, and educating the public about such issues is a particular challenge. However, the documentary genre is growing in popularity because it relies less on scientific, political or business discourse, and is historically associated with educating the public. Further, the environmental documentary relies heavily on visual rhetoric, which can influence viewers’ awareness and willingness to act in eco-conscious ways. This presentation draws upon my doctoral research, which explored visual rhetorical modes in environmental documentaries, and what types of visual rhetoric most influenced viewers. The presentation will focus on participants’ response to jeremiad rhetoric (attributing human behaviour as the cause of environmental crisis) employed in the clip from Our Daily Bread (2005). While unnerving, the jeremiad visual rhetoric of Our Daily Bread did make an impact on my participants, as most spoke to the cruelty of commercialized farming, and noted the disconnection between commercialized food consumption and production.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".