Digital Storytelling to Overcome Disruption: Exploring Local Heritage in an Online Environment
Bibliographic record
Abstract
This study investigates how digital storytelling (DS) functions as a communicative strategy for the online presentation of local heritage. It aims to understand the extent to which DS mitigates digital disruption and supports inclusive, participatory, and identity-based narratives. The research applies a qualitative discourse analysis, complemented by a quantitative scope analysis, to a selected sample of 57 websites containing local heritage content. The analytical framework is organized into four levels of discourse analysis: textual, contextual, interactional/action-based, and ideological. The websites were evaluated according to narrative structure, emotional tone, multimodality, user interaction and curatorial authority. DS was identified in 60% of the websites in implicit, explicit or combined form. These DS strategies frequently include multimodal formats (text, audio, video, and image), personalized expression and emotionally resonant language. A recurring paradox was observed: while visual design is becoming more minimalistic, narrative richness is increasing. Most websites exhibited integrative and micro-historical approaches; however, a subset retained institutional or canon-based frameworks that limit narrative diversity. Methodologically, the study underscores the need to adapt discourse analysis to multimodal and non-linear environments. It also highlights the significance of curatorial power in shaping digital heritage narratives. The findings are relevant to libraries, cultural institutions and digital curators seeking to enhance user engagement and inclusivity through narrative strategies. This research contributes to digital heritage, information science and internet studies by considering DS as a form of socio-technical resilience that supports meaning-making, identity and cultural memory in digitally mediated environments.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".