Hybrid Approaches for Smart Contracts in Land Administration: Lessons from Three Blockchain Proofs-of-Concept
Bibliographic record
Abstract
The emergence of “blockchain” technology as an alternative data management technique has spawned a myriad of conceptual and logical design work across multiple industries and sectors. It is also argued to enable operationalisation of the earlier “smart contract” concept. The domain of land administration has actively investigated these opportunities, albeit also largely at the conceptual level, and usually with a whole-of-sector or “big bang” industry transformation perspective. Less reporting of applied case applications is evident, particularly those undertaken in collaboration with practicing land sector actors. That said, pilots and test cases continue to act as a basis for understanding the relative merits, drawbacks, and implementation challenges of the smart contract concept in land administration. In this vein, this paper extends upon and further refines the existing discourse on smart contracts within the land sector, by giving an updated, if not more nuanced, view of example applications, opportunities, and barriers. In contrast to the earlier works, a hybrid solution that mixes smart contract use with existing technology infrastructure—enabling preservation of the role of a land registry agency as the ultimate arbiter of valid claims—is proposed. This is hypothesised to minimise disruptions, whilst maximising the benefits. Examination of proof-of-concept work on smart contract and blockchain applications in Sweden, Australia (State of New South Wales), and Canada (Province of British Columbia) is undertaken. Comparative analysis is undertaken using several frameworks including: (i) business requirements adherence, (ii) technology readiness and maturity assessment, and (iii) strategic grid analysis. Results show that the hybrid approach enables adherence to land dealing business requirements and that the proofs-of-concept are a necessary step in the development trajectory. Furthering the uptake will likely depend on again taking a whole-of-sector perspective, and attending to remaining issues around business models, stakeholder acceptance, partnerships and trust building, and legal issues linked to data decentralisation and security.
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".