Bibliographic record
Abstract
To reach younger audiences, museums worldwide have incorporated interactive and hands-on activities, while some venues specialise in children as their main audience. Videos, in particular, can be easily integrated in the museum space and provide a variety of application possibilities. Their use creates a hybrid experience for the visitor in which the interaction between physical and digital elements transforms and enriches their experience of the exhibits. Furthermore, the use of interactive technologies has been proven to increase visitor numbers and interactions on- and off-site. In this context, our research focuses on the use of interactive video technologies, and factors that can lead into the design of engaging and user-friendly museum experiences for children. To achieve this, a museum was chosen as a case study and a survey was conducted. The results indicated that the creation of an interactive video could benefit the areas that were visited less; the preferable length is rather short; while hands-on and video installations promote and prolong the engagement of young visitors, and are favoured by both younger and older children. Additionally, fictional or dramatised stories are attractive to children compared to documentaries; and it would be preferrable to access the interactive content on their mobile devices. These have led to the production of Paintings Alive, an interactive film for children, based on the museum’s art gallery, and accessible on the visitors’ mobile devices. Our paper also discusses the findings of the project, alongside the challenges and limitations imposed by the COVID-19 pandemic, and offers recommendations for future work.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.144 | 0.026 |
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".