Globally Networked Learning Environments: Reshaping the Intersections of Globalization and E-Learning in Higher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As many e-learning scholars have emphasized, e-learning – situated in a global network of digital technologies – has, of course, a complex global dimension that manifests itself in diverse ways in different institutional, disciplinary, national, and other local academic and educational traditions as digital technologies intersect with local educational practices, policies, and pedagogies. Accordingly, many e-learning scholars have placed this global dimension of e-learning and its local manifestations at the heart of their scholarship, with Lam (2009), for example, examining the literacy practices of immigrant teenagers in online environments; Al-Fadhli (2008) exploring the perceptions of e-learning at Kuwait University; and Marumo et al (2009) studying the role of an elearning platform for educational innovation in Botswana. Others have compared information and communication technology knowledge and usage through the lens of gender and class in Ghana (Kwapong, 2009); studied the role of e-learning in an early childhood programme offered at a virtual university in Africa (Pence, 2007); examined the role of new literacies in the teaching of
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it