Towards a model for comprehending and reasoning about PoW-based blockchain network sustainability
Bibliographic record
Abstract
Blockchain networks have been claimed to have the potential of fundamentally changing the way humans perform economic transactions with each other. In such networks, trust-enabling agents and activities, that were traditionally arranged in a centralized fashion, are replaced by a network of nodes which collectively yet independently witness and establish the non-repudiability of transactions. Most often, a proof-of-work (PoW) requirement ensures that participants invest resources for joining the network, incentivizing conformance to the network rules, while making it highly infeasible for malicious agents to construct an alternative version of the transaction history. While research on security and efficiency aspects of blockchain networks is already being conducted, there is still work to be done to understand how different external and internal conditions guarantee or threaten their sustainability, i.e., their continuous operation. Focusing on public PoW-based blockchain platforms, in this paper we sketch an abstract model that is aimed at supporting comprehension and qualitative reasoning about the factors that affect sustainability of a blockchain network.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".