Bibliographic record
Abstract
Micro-credentials can provide easily accessible and transparent evidence of skills or knowledge that have been certified by an authority, based on small units of learning. The recognition and transfer of credits is becoming essential, as an increasing number of students are studying at different institutions, often at the same time, online or in traditional settings. "Blockchain is a type of database that stores data in an "open, peer-to-peer (P2P) network that favors communal functionality in lieu of a centralized controlling entity" (Columbia Engineering Bootcamps, 2021, para 5). The development of blockchain (https://theconversation.com/demystifying-the-blockchain-a-basic-user-guide-60226) holds promise of becoming a useful facilitator for supporting the storage and dissemination of micro-credentials on a global scale. Besides providing effective data security and privacy, blockchain can also facilitate maintaining and disseminating credentials, while ensuring that access is readily available for students under their control. Because of its immutability, blockchain can be used to confidently attest to students' accomplishments and is therefore particularly appropriate for micro-credentials.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".