The usefulness of intellectual property rights in selected areas of museum activity
Bibliographic record
Abstract
As intangible resources, intellectual property rights are difficult to measure. The search for ways to measure and research them and their impact on the current activities of nonprofit organizations such as museums is important because it translates into the effectiveness of the institution’s cooperation with the environment and the stabilization of the organization over time. I aimed to identify the usefulness of intellectual property rights in the context of three main areas of museum activity: heritage collection and preservation, research and education, and sharing and access to heritage. To achieve the goal, I conducted empirical research among museum employees from Poland and other European countries, Australia, New Zealand, the USA, and Canada. A total of 190 respondents completed the survey. I determined the usefulness of intangible resources not by the characteristics of these resources, but by the key areas of museum activity resulting from the ICOM definition. Research results indicated that the most useful intellectual property rights were copyrights to collections. In selected areas, rights to employee works, databases, and know-how, namely implicit specialized knowledge resulting from employee experience, were also useful.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".