Education and the social Web: Connective learning and the commercial imperative
Bibliographic record
Abstract
In recent years, new socially-oriented Web technologies have been portrayed as placing the learner at the centre of networks of knowledge and expertise, potentially leading to new forms of learning and education. In this paper, I argue that commercial social networks are much less about circulating knowledge than they are about connecting users (“eyeballs”) with advertisers; it is not the autonomous individual learner, but collective corporate interests that occupy the centre of these networks. Looking first at Facebook, Twitter, Digg and similar services, I argue their business model restricts their information design in ways that detract from learner control and educational use. I also argue more generally that the predominant “culture” and corresponding types of content on services like those provided Google similarly privileges advertising interests at the expense of users. Just as commercialism has rendered television beyond the reach of education, commercial pressures threaten to seriously limit the potential of the social Web for education and learning.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".