A Permissioned Blockchain-Based System for Verification of Academic Records
Bibliographic record
Abstract
While academic institutions maintain records such as transcripts and certificates, they are often requested to share these records with other institutions at the request of students for credit transfer, or prerequisites for acceptance into new academic programs. While the transfer of academic records is a regular daily activity for the institutions, there is often significant overhead involved as the process of transfer and verification is extremely manual. The need for an automated end-to-end solution for the transfer and verification of academic records between institutions is on the edge to reduce wait times for students to transfer their records, as well as to provide a reliable verification method to avoid academic fraud. This paper presents a permissioned blockchain-based system to allow institutions to securely and dependably transfer and verify academic records at the student request. Permissioned blockchains, such as Hyperledger, provide a more scalable and cost-effective and private solution for enterprise applications. Our solution is comprised of a web interface for enrolling and requesting the transfer, with a backend using Hyperledger Fabric and Hyperledger Composer to retain the hash of the records on the blockchain for verification.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".