Bibliographic record
Abstract
Educational scholars have highlighted how social media platforms can be powerful sites of learning and organizing for social change. Research into informal social media learning has suggested that it promotes life-wide education, offering opportunities to learn through networks of connection, co-create knowledge with others, and foster self-directed learning. Studies of social media-based activism, for instance linked to movements like Black Lives Matter and Me Too, have demonstrated that it leads to the acquisition of subject-matter expertise, development of capabilities such as organizing for change, development of capacity for self-reflection, and personal transformation such as increased belief in one’s ability to shift political systems. However, universities are yet to fully leverage the affordances of social media for learning. Many institutions offer online classes, and many individual instructors utilize social media in their teaching. Yet, university learning has not transformed to a degree that is commensurate with the radical ways in which, in our post-digital era, learning has come to be transformed in society at large. In this context, a question to be asked is why universities, those institutions charged with educating individuals for personal and societal benefit, stake such minor claims in what has emerged as an integral learning space of our era. Universities’ slowness to embrace change means they are abdicating important spaces of learning, connection, and change to private corporations like X and Facebook, which lack both the skills and motivation to promote learning in the public interest. This presentation argues for a university-owned social media platform, which would use the media’s affordances to advance a post-classroom vision of learning; classes would continue to be part of the educational experience, but they would be decentred as the primary site of learning. Further, this presentation tells of the presenter’s work in exploring the possibility of prototyping such a platform within their own university. It builds on Networked Learning (NL) scholarship, specifically expanding Lee and Bligh’s (2023) expanded design framework for transformative NL, and drawing inductively on relevant NL literature to put forward six design principles for a university owned platform. The platform would connect formal and informal learning. It also would connect learners, staff, and faculty to each other and to the community outside the university. It would be comprised of three virtual spaces, dedicated to: (1) learning and organizing for social change; (2) student-initiated community building and learning; and (3) university-led news, events, and discussion.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".