Insufficient Understanding of User Benefits Impedes Open Data Initiatives at Museums
Bibliographic record
Abstract
A Review of: Booth, P., Navarrete, T., & Ogundipe, A. (2022). Museum open data ecosystems: A comparative study. Journal of Documentation 78(4), 761-779. https://doi.org/10.1108/JD-05-2021-0102 Objective – Using Nardi and O’Day’s (1999) definition of ecosystem as “a system of people, practices, values, and technologies in a particular local environment,” to understand how art museums form their policy to interact with and respond to the various open data (OD) ecosystems in which they operate. Design – Multiple case study consisting of interviews and subsequent qualitative analysis, as well as document analysis. Setting – European art museum OD ecosystems. Subjects – Subjects included 7 management staff members at 3 separate mid-size, art-based museums located in Norway, the Netherlands, and Spain; an unspecified number of representatives from a cultural-policy agency in each of those countries; an unspecified number of government, museum, and research documents from within each museum’s OD ecosystem. Methods – The researchers identified 3 museums with OD initiatives and conducted in-depth interviews with relevant staff members at each institution. The researchers also interviewed representatives from relevant national OD policy-related agencies. The researchers coded their data and developed a list of five key OD “ecosystem components,” which they used to analyze the 3 specific museum ecosystems under consideration. Main Results – Open data initiatives at cultural heritage institutions are subject to a number of internal and external pressures. Museums are typically responsive to their environments, and top-down policy requirements appear to be an effective means of advancing open data initiatives. Nevertheless, the value proposition of open data appears to be insufficiently understood by museum staff and other stakeholders. As a result, museums participate in OD initiatives even when the benefit remains undemonstrated and the use of OD—how and by whom—remains unclear. Conclusion – The needs and wants of OD end-users remain ill-defined and poorly understood. As a result, museums expend resources and effort to supply OD, while remaining uncertain about the return on their investment. Attention to users could result in “more robust information flows between ecosystem components.”
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 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 | 0.100 | 0.204 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.027 | 0.040 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".