Challenges and Opportunities for use of Social Media in Higher Education
Bibliographic record
Abstract
Likely the most significant and life changing technologies of the 21st Century is the adoption of social media as major components of commercial, entertainment and educational activities. In this article, I overview the supposed benefits of the application of these tools within formal higher education programs. I then discuss the disadvantages and challenges, with a focus on the paradox that accompanies convenience and value in use, with loss of data control. It is likely that we will continue to see both authorized and unauthorized use of data that we have created for both personal and institutional use. I conclude by examining some of the solutions proposed and tested to resolve this challenge. I then overview two possible solutions - the first focused on institutions creating and managing their own social media and the second an emergent technical solution whereby users keep control of their data, while sharing and growing in multiple social contexts.
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 | 0.044 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.028 | 0.034 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".