Bibliographic record
Abstract
The emergence of cloud computing has changed the ways of thinking, communicating, performing professions, and maintaining sociocultural and community relations. The capacious cloud storage and its amazingly growing facilities and capabilities in virtualizing human activities and the entire phenomenal entities and in synchronizing them with new digital cloud technologies such as laptops, tablets, smartphones or mobile phones, and personal computers (PCs) have not only increased human capabilities, but also added new creative dimensions to sociocultural, economic, political, epistemological, ontological, and educational fields. The ways of producing, sharing, and acquiring knowledge, teaching, and learning have been profoundly changed. So this chapter first defines what a cloud-enabled learning environment refers to and critically examines how cloud computing optimizes learning opportunities and transforms teaching and learning pedagogies. Then, it critically explores how cloud-enabled learning environments and cloud-based pedagogies can address the gaps in education caused by the digital divide, how cloud-assisted networks of local learning-hubs can contribute to the success of global literacy campaigns, and how cloud computing reaffirms the significance of distance learning or massive open online course (MOOC) and cloud-assisted practices of teaching self. The rapid shifts in pedagogical grounds from non-virtual paradigms into the virtual world or the cloud clearly indicate that in the future teaching and learning activities and pedagogies become more cloudocratic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".