EPIC visuals: An integrated framework to operationalize archetypes for visual storytelling
Bibliographic record
Abstract
Compelling visuals are vitally important for successful brand storytelling. Yet even though the importance of visual content has exploded in recent years – driven by the ease through which consumers, influencers, and managers can create and share brand-related visuals thanks to social media and now generative artificial intelligence – comprehensive advice on how brand strategies can be visually executed has remained scarce. This article introduces the EPIC framework for translating nuanced brand meanings into concrete visual content. Integrating the literatures on visual storytelling, archetypes, and critical visual analysis, the framework details four interlocked steps to operationalize archetypes for visual storytelling: Defining the essence of the brand, personifying the brand essence into suitable archetypes, inflecting archetypes towards the brand via themes, and cataloguing visual elements into a SMART instrument to guide visual content creation in a bottom-up process. Brands that employ the EPIC framework can bridge the strategy-execution gap in visual storytelling and unlock two particular benefits: (1) a more consistently enacted archetypal gestalt, and (2) differentiation through more distinct and clearly composed images.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".