Digital Skills And Education In The Cultural Heritage Sector : Post-COVID Digital Transformation of Galleries, Libraries, Archives and Museums
Bibliographic record
Abstract
According to UNESCO (2021) 43% of museums faced closure in the first quarter of 2021 due to an ongoing pandemic. Those that could, offered digital experiences to maintain links with their visitors, and for those institutions without the skills or resources to reach out to their visitors, their very existence remained in a state of flux. The situation in Sweden was no different and, prior to UNESCO’s published figures of 2021, the Swedish Government (2020) instigated an inquiry to establish the necessary interventions required to reignite the heritage sector post pandemic. The inquiry found a history of challenging problems for the sector. One problem recognised was the need for a digitalised sector to sustain heritage, and secondly, the motivation for this research, the lack of skills and resources in the sector. The aims of this research therefore were twofold. Firstly, to identify what digital skills andeducation are needed in galleries, libraries, archives, and museums, (GLAM) and secondly, to test the hypothesis that Higher Education curricula in Sweden requires a programme of analysis and restructure. Notwithstanding a review of past literature review to reflect on previous research of skills and education in the sector, an in-depth analysis of the Governmental inquiry also took place. As a secondary research activity, a sample of respondents were selected to represent small and large institutions across the sector. Comments, experiences, and opinions regarding lack of resources and skills, were extrapolated from recorded interviews, surveys and responses to the Governmental Inquiry which were noted and logged. Secondly, an analysis of skills required in current job vacancies in the sector took place. Additionally, perspectives from sector unions were sought, and finally, a limited survey of employee thoughts on current skills and what skills they believed they needed for the future was completed. The results indicated that discourse of skills and the digital vision in the sector have been ongoing for many years and that a global pandemic had to be the instigator for governmental intervention. There is also a traditional approach to the education of graduates in galleries, archives, and museums, unlike those educated to work inlibraries. archives and researchers via digital humanities programs. For those entering the profession in museums and galleries, there is little, if any exposure to digital practices. Barriers to change included financing, infrastructures, and a lack of leadership development in the sector. Digital transformations it was felt, requires a top-down approach and leadership is currently not in situ.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".