The Socio-Cultural Dynamics of Blockchain Platforms: Beyond Decentralization and Algorithms
Bibliographic record
Abstract
This symposium explores the sociocultural dynamics of blockchain-based organizations as emerging forms of organizing. It examines the multifaceted challenges these organizations face in achieving and sustaining legitimacy and fostering growth within the evolving blockchain field. By shifting focus from purely technical aspects of decentralization to the socio-cultural mechanisms embedded in blockchain governance, the symposium highlights the interplay of symbolic and material realms, configurational logics, and societal legitimacy. Drawing on diverse organizational theories and qualitative methodologies, including netnography, rhetorical analysis, QCA, and topic modeling, this symposium seeks to advance a deeper understanding of blockchain governance, emphasizing its socio-cultural embeddedness, accountability, and sustainability. Participants will engage with new theoretical and methodological approaches to unpack the complexities of blockchain platforms beyond decentralized algorithms, offering insights into their growth, coordination, and integration within broader institutional contexts. Beyond the Code: A Configurational Analysis of Blockchain Governance Resilience to Challenges Author: Yunjung Pak; University of Alberta Accountability in Blockchain-Enabled Organizations: Insights from Decentralized Autonomous Organization Author: Nina-Birte Schirrmacher; The University of Sydney Author: Michel Avital; Copenhagen Business School Author: Johannes Rude Jensen; Author: Omri Ross; - The Sociocognitive Formation of Category Stigma: The Case of Blockchain Voting in U.S. Elections Author: Gianlorenzo Meggio; Aarhus University Author: Agnes Radziwon; Constructing a Technology Logic: Multimodal Prefiguration and Hype in Blockchain for good Author: Paula Ungureanu; Author: Carlotta Cochis; University of Modena and Reggio Emilia
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".