Collective Data Ecosystem Governance and Design:Tensions, Data Quality, and Social Welfare Implication
Bibliographic record
Abstract
The purpose of this symposium is to bring together scholars to discuss governance models and design of data ecosystems in a decentered way from different societal and scholarly perspectives. We will focus on the specific opportunities and challenges involved in data governance in the absence of a solo (or dominant) owner of a data ecosystem exercising power by prioritizing its own interests over other actors through proprietary technological architecture, and threatening social welfare. However, the complexity of organizing an array of heterogeneous actors with a variety of expectations and motives threatens the sustainability of ecosystems. This symposium aims at providing a deeper understanding of design and governance tradeoffs in developing decentered data ecosystems from strategic, organizational, innovation, infrastructural, ethical and regulatory perspectives. Building on presented papers, this symposium serves as a platform to discuss decentered bottom-up data governance challenges and issues. Decentralized Data Ecosystems as Meta-Organizations Author: Niloofar Kazemargi; U. of Chieti-Pescara Author: Fatemeh Saadatmand; IT U. of Copenhagen A Grounded Theory of Data Connections in Healthcare Author: Daniel Fürstenau; IT U. of Copenhagen Author: Anne-Katrin Witte; U. of Hagen Author: Till Winkler; Copenhagen Business School Ego-system or Eco-system? Author: Nina-Birte Schirrmacher; Vrije U. Amsterdam Adverse Effect of an Open Data Program on Data Quality Author: Mahdi M Najafabadi; California State U.
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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 | 0.068 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.028 | 0.029 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".