Globalization, Technologies, and Digital Culture in Graduate Contexts: Intercultural Possibilities and Challenges
Bibliographic record
Abstract
This study focused on interculturally juxtaposing different higher education communities’ experiences with digital culture and technologies. The dialogues created among researchers from three different countries – Brazil, Canada, and the UK – contribute to an exchange of reflections and problematizations of what innovative and ubiquitous pedagogical practices are like. For the past few years, especially due to COVID-19, researchers have identified the impact of digital culture on educational practices in different universities, highlighting there is a need to further understand the relationship between the advancements in digital culture and its outcomes for innovative educational practices. The participants in the study helped the research team to consider the possibilities and challenges of digital culture in education by sharing perspectives on: 1) the conception educational communities in universities have about innovation, educational practices and digital culture; and 2) the relationship of instructors, students, and other members of the educational community (e.g.; secretaries, deans, head of departments) toward educational practices that include innovation, and digital culture in their day-to-day practices. Our discussions broadened the notions of innovation, and digital culture in educational practices by inviting professionals from universities from different contexts to reflect on intercultural aspects that shape new dialogues to negotiate tensions among educational practices within digital culture. Keywords
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.042 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".