Bibliographic record
Abstract
Climate-related catastrophes and wars are leaving people in need of aid. The main obstacle in providing help to people in need is the lack of trust in aid processes. Donors and charity organizations want to make sure that funds and materials gathered reach the intended destinations. The lack of proof leads to a general sentiment of waste, corruption and misuse, which undermines aid efforts. Blockchain technology injects trust into the business transactions through impeccable record keeping and fulfils the lack of trust problem in aid delivery. However, our review of relevant literature indicates that a delivery assurance framework that covers major aspects of providing a blockchain-based solution to aid delivery is absent. In this thesis, we propose a novel blockchain-based transparent delivery framework for creating solutions that record and share data on the interaction of business participants involved in a delivery process. This framework is novel as it creates solutions that include handover and monitoring aspects of the delivery business and adds several benefits that come with the blockchain technology. This delivery assurance framework also provides complete guidance as it answers several key questions such as “How can we use blockchain technology to solve problems?” and “How can we make sure the solutions are financially viable and acceptable?” Our simulation study validates the applicability of our framework and the solution we created using the framework. Further, the validation we received from an industry expert strongly suggests that a solution developed with our framework is applicable in industry. This thesis presents the development of the framework along with details on the design and execution of our simulations, including the raw data, data enhancement processes, tools, data structures, smart contract code, load testing methodology and the eventual analysis of the simulation results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".