Pervasive, disruptive, seductive, enabling: Designing technologies for learning and social innovation
Bibliographic record
Abstract
"There are no technological revolutions without cultural transformations." This is a quote from the book “The Internet Galaxy: Reflections on the Internet, Business, and Society” by Manuel Castells, in which the author explores the complexity of the social problems generated by the spread of the Internet. Nowadays the Internet is no longer simply a means for connecting people through computers. The digital components of the network have materialized in things. Information has ceased to travel exclusively on the computer screen and moved onto physical objects, now able to talk to each other and with the environment. The challenge is that this technological innovation will become a social innovation, and that individuals, society, institutions and companies will appropriate it, modifying it, transforming it, and experimenting with it. This paper is a reflection on the role of technology in supporting social innovation. We will approach this topic from the perspective of interaction design, a discipline that studies social practices connected with use of technologies, and imagines new possibilities as well as new activities enabled by them. The reflection will develop by presenting the outcomes of Light through Culture, an international educational project that aims to create a meaningful context for learning in which students reflect on socio-cultural issues together by building interactive installations.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".