Digitizing Borderless Higher Education Landscapes Through Curriculum Policy Change to Educate Global Citizens
Bibliographic record
Abstract
Advancements in science and technology are mobilizing higher education landscapes into borderless settings. Such changes also urge higher education settings to adopt transformative learning opportunities into their curriculum policy. Universities are accountable for helping youth build on their 21st Century competences by highlighting societal issues at global levels such as climate change, refugee crises or big human movements due to poverty, politics, conflicts, wars, or natural disasters. Youth need to build on knowledge, skills, and competences to recognize that any crises in one location can have an immediate impact on neighboring countries primarily and the whole world and challenge their potential to act as global citizens in their deeds and decisions as future change agents for a peaceful future. In this paper, we highlight the need to invest in global citizenship capabilities that will enable higher education students to go beyond their academic settings and network with international students via digital tools. In this paper, higher education students are regarded as future change agents who are willing to develop accountability toward the entire globe by investing in their socio-ecological, socio-critical and socio-emotional capabilities.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".