Media Art on Wikipedia: Leveraging the Wikimedia Ecosystem to Address the Challenges and Opportunities of Media Art Institutions
Bibliographic record
Abstract
“Media Art on Wikipedia” is a collaborative project launched by Amsterdam-based platform for media art LIMA (Living Media Art), conducted in partnership with national and international art collections, national universities, and Wikimedia Nederland. Involving coordination of edit-a-thons and ingest of collection metadata within the structure of Wikidata, the project aims to bridge and mobilize the domain knowledge of media art beyond the institutional silo through engagement with the ethos, procedures and affordances of the Wikimedia ecosystem. This article profiles and situates this attempt within the particular institutional context of media art and the broader turn towards open, participatory, and distributed configuration of public institutions. Comparing infrastructural topologies identified within the discourse of media art by mapping them to tensions between centralized and distributed approaches, this article positions the project as a composite of models blurring their unambiguous theoretical distinction. This analysis serves to investigate ways in which the project responds to the conditions underlying the field’s modes of knowledge production—on one hand the demand for the reimagining of methods of documentation, preservation, and historicization stemming from the specificity of media art; and the systemic precarity of media art’s knowledge infrastructures on the other.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".