Information design and digital curation at the museum of the person
Bibliographic record
Abstract
Web 2.0 has presented challenges and possibilities for cultural facilities and information professionals. Thinking strategically about such aspects implies creating openings for the collaboration of communities of interest in the identification of informational objects, and converging concepts, methodologies and techniques of information design, digital curation and folksonomy, which would address the challenges that emerge in the process of digitization-virtualization and availability of collections on the Web. Given this, cultural facilities must get closer to the communities of interest, as is done by the Museum of the Person (Museu da Pessoa), a digital-virtual and collaborative museum whose collection is composed of life stories. The museum uses the social technology of memory, a methodology that consists of a set of practices, concepts and principles essential for diverse communities of interest and institutions to take ownership of the production and recording of narratives. The present study asks how folksonomy, seen as a resource of information design and digital curation, can converge to the social technology of memory? Thus, the objective is to analyze the possibilities of convergence of folksonomy, information design, and digital curation to the social technology of memory. The specific objectives are to study the main concepts of folksonomy in the context of information design and digital curation, analyze the social technology of memory, and identify possible convergences of folksonomy in methodology. The methodology applied in the present study is theoretical, exploratory, and qualitative, and the method applied was design thinking.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".