Bibliographic record
Abstract
Data-based public art is an innovating new form of digital art which presence is increasing in the cities datascape. Data as a medium provides a special relationship with time and space by connecting the context of data mining to the one of its exhibition. The virtual component of data art opens an augmented space, where the different dimensions of data are mediated. This essay analyses how this new artform can contribute to a creative and digital placemaking of a city by offering a special sensory experience as well as renewing the storytelling of its space. Three case studies support the analysis. “Living connections”, projected on an emblematic bridge in Montreal, contributes to a spectacular placemaking. “Interconnected”, a data sculpture in Charlotte airport, relates to infrastructure placemaking. Finally, “Herald / Harbinger” connects the industrialized society with nature in a global connection. The results participate to the reflection on the nature and specificity of data art as well as enhancing its potential of transforming public space by engaging a specific relation with time, place and people.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.027 | 0.016 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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".