Key Issues and Future Trends of Digital Cultural Policy in the Post-Pandemic Era
Bibliographic record
Abstract
Digital transformation has become a vital concept in many global agendas ever since Klaus Schwab discussed the Fourth Industrial Revolution at the 2016 World Economic Forum. The digital surge during the COVID-19 pandemic has induced more significant impacts on every facet of our society and has imposed countering policy measures on policymakers and governments to cope with the crisis. This research aims to identify the current trends and issues of cultural policies in the digital environment at a global level. By adopting the conceptual framework of cross-national policy transfer (<xref ref-type="bibr" rid="B20">Dolowitz & Marsh, 2000</xref>) and three opportunities to drive purpose-led digital transformation (<xref ref-type="bibr" rid="B51">World Economic Forum, 2020</xref>), this study engages with a qualitative research design by conducting document analysis based on policy reports and international policy cases published by the Organisation for Economic Co-operation and Development and United Nations Educational, Scientific and Cultural Organization. Additionally, the authors have selected two countries for their case study, Canada and the UK, to review their digital cultural policy plans that were initiated before the pandemic and were well-received. This research suggests that, while the arts and creative sectors should adapt to the new digital environment from technologies to business models, potential inequalities, such as cultural representations and market-oriented consumptions, must also be monitored and prevented. For sustainable development of the arts and cultural domain, public agencies also need to invest and promote innovative collaborations and networking and experimental arts projects and research. Further, they must develop new business models for arts and creative organizations with more open and user-driven policy measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".