Social Media for Informal Learning: a Case of #Twitterstorians
Bibliographic record
Abstract
Open, online environments like social media are now a mainstay of life-long informal learning. Social media like Twitter help people gather information, share resources, and discuss with other participant-learners with similar interests. This paper seeks to test and validate the ‘learning in the wild’ coding schema in the context of discussions on Twitter, an approach first developed for studying learning communities on Reddit. The schema considers how participant-learners are leveraging social media to facilitate self-directed informal learning practices, exploratory dialogue, and communicative exchanges. We apply the coding schema on a sample of tweets (n=594) from the History Twittersphere community (#Twitterstorians) to provide a more nuanced understanding of the different kinds of discursive practices, resource exchanges, and ideas being shared and communicated outside traditional classroom settings.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.016 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".