Open a GLAM lab
Bibliographic record
Abstract
Defining a GLAM Lab: A Galleries, Libraries, Archives and Museums (GLAM) Lab is a place for experimenting with digital collections and data. It is where researchers, artists, entrepreneurs, educators and the interested public can collaborate with an engaged group of partners to create new collections, tools, and services that will help transform the future ways in which knowledge and culture are disseminated. The exchanges and experimentation in a Lab are open, iterative and shared widely. This book describes why and how to open a GLAM Lab and encourages participation in a movement that can transform organisations and the communities they partner with. Building a GLAM Lab: Building a GLAM Lab involves defining its core values to guide future work, fostering a culture that is open, transparent, generous, collaborative, creative, inclusive, bold, ethical, accessible and encourages a mindset of exploration. The Lab should be grounded in user-centred and participatory design processes and its staff should be able to clearly communicate what the Lab is about. It's important to think big but start small and establish quick wins to get up and running. GLAM Lab teams: There are recommendations for the qualities and skills to look for in Labs teams, how to go about finding allies within and outside the institution, and ideas on how to create a nurturing environment for teams to thrive in. Labs teams have no optimal size or composition, and its team members can come from all walks of life. Teams need a healthy culture to ensure a well-functioning Lab which might be augmented intermittently by fellows, interns or researchers-inresidence. For a Lab to have lasting impact it must be integrated into the parent organisation and have the support of staff at all levels. User communities: GLAM Labs will need to engage and connect with potential users and partners. This means rethinking these relationships to help establish clear and targeted messages for specific communities. In turn, this enables Labs to adjust their tools, services and collections to establish deeper partnerships based on co-creation, and open and equal dialogue. Rethinking collections and Data: The book discusses the digital collections which are an integral part of Labs. It provides insights on how to share the collections as data, and how to identify, assess, describe, access, and reuse the collections. In addition, there is information about messy and curated data, digitisation, metadata, rights and preservation. Transformation: Experimentation is the critical core of the Lab's process. Insights about how to transform tools into operational services are demonstrated. It shows that experimentation can prepare the organisational culture and services for transformation. There is an examination of funding and the advantages and disadvantages of various models through discussion of the different mechanisms and options that an organisation can apply to Lab set-ups. Funding and Sustainability: We share insights on how to plan for a Lab's sustainability as well as a step-by-step guide for when an organisation is retiring or decommissioning a Lab. Labs have a pivotal role in the transformation of GLAMs and the book highlights the critical importance of Labs in changing the future of digital cultural heritage.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.023 | 0.019 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".