The Human Side of Digital Technology: Supporting the Inclusion of Refugees in Higher Education Through Blockchain-backed EQPR
Bibliographic record
Abstract
The spectrum of digital technologies that impact Higher Education (HE) is broad. This study explores the potential of Blockchain (BC) technology in the personalized learning path creation and data exchange in the learning processes. The flexibilization of education and digitizing student data through Blockchain may contribute to a more inclusive and sustainable HE system. According to the EU, the Blockchain supports sustainability in four main aspects: cybersecurity, accountability, transparency, and traceability. These aspects are also a driver of social impact and a higher capacity to include disadvantaged groups, such as refugees. Therefore, it is essential to start a debate between scholars and professionals about how the actors in the HE system engage in a collective meaning-making effort to sustain the adoption, diffusion, and use of BC for HE. The paper focuses on the experience of CIMEA DiploMe and EQPR for the recognition of refugees' qualifications. Through a collective consensus-making and awareness-raising effort, the blockchain-backed EQPR could be perceived as a critical tool to foster inclusion within the HEIs and enhance their social outreach.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".