Metaphoric Interpretation of the Actuality: Representing Subjectivity Using Creative Visual Metaphors in Animated Documentary
Bibliographic record
Abstract
Metaphors play a significant role to construct a complex narrative of documentary subjects through semiotic language broadening the scope of the visual narrative strategy of animated documentary concerning its relevance with embedded subjectivity and emotions. Looking through the lens of conceptual metaphor theory (Lakoff and Johnson, 1980), the current paper explores the practice of creative metaphors representing complex human emotions through subjectivity perspective in the animated documentary as conjecturally weaved animation of embroidered artworks by Kutch artists in Nina Sabnani’s The Stitches Speak (2010) and as an abstract illustration of emotive experiences of alcohol-addict Canadian artist, Ryan Larkin, in Chris Landreth’s Ryan(2004). Employing the metaphoric analysis approach (Moser, 2000), both the films are examined to understand comprehensive metaphorical stance portraying discrete subjective phenomena of human history. In this paper, I argue that the creative visual metaphors endorse diverse interpretations of psycho-social ecology of the ‘subjectivity’ under consideration corresponding to a broader understanding of the embedded emotions of the subject/s. The introduction of the metaphors also conceptualizes amplified creative narrative for enhanced visualizations and perception.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 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".