Bibliographic record
Abstract
In the years of writing about Web sites for this publication and for its predecessor, Technology Source, I have resisted reviewing sites in which I have been directly involved.This issue, however, I will make an exception in order to introduce the reader to the Connectivism & Connective Knowledge online course that is offered through the Learning Technologies Centre and Extended Education at the University of Manitoba by George Siemens and me.The course is part of the program leading to the Certificate in Adult and Continuing Education (CACE).The design and content of this course captures some trends that have emerged in recent years in the field of online learning.As suggested by the concept I have called "e-learning 2.0" (Downes 2005 ), online course offerings should move away from large, centralized applications and instead make use of a network of connected applications.Connectivism & Connective Knowledge is not simply about the use of networks of diverse technologies; it is a network of diverse technologies.One participant in the course captured the network-like structure of the course in a diagram that shows how the different elements and various technologies are linked together (Figure 1).Each of the elements described in this review has some place in this image.
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.
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 | 0.001 | 0.004 |
| 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 | 0.022 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".