Digital curation and the networked audience of urban events
Bibliographic record
Abstract
The proliferation of portable, networked and location-aware devices has drastically changed how the city is represented and interpreted in general and during specific events in particular by enabling new practices of digital curation and networked audience activities. These extend the urban realm from the physical into the virtual, which provides a space for global and dispersed, often naive audience activities. This article uses the case study of the Fiesta de Santo Tomás, which is an annual festival that takes place during the week leading up to Christmas in Chichicastenango, Guatemala, to illustrate how digital curation, (re)presentation and (re)interpretation of festive events occur in a hybrid urban space. By documenting the ways that the modern day version of this festival has made its way into the larger digitally mediated sphere of urbanism, the study looks at three groups of curators and how their ways of encoding the event provide a multiplicity of representations to be decoded by the members of the ephemeral networked audience.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".