Learning in the wild: coding for learning and practice on Reddit
Bibliographic record
Abstract
Learning on and through social media is becoming a cornerstone of lifelong learning, creating places not only for accessing information, but also for finding other self-motivated learners. Such is the case for Reddit, the online news sharing site that is also a forum for asking and answering questions. We studied learning practices found in ‘Ask’ subreddits AskScience, Ask_Politics, AskAcademia, and AskHistorians to develop a coding schema for informal learning. This paper describes the process of evaluating and defining a workable coding schema, one that started with attention to learning processes associated with discourse, exploratory talk, and conversational dialogue, and ended with including norms and practices on Reddit and the support of communities of inquiry. Our ‘learning in the wild’ coding schema contributes a content analysis schema for learning through social media, and an understanding of how knowledge, ideas, and resources are shared in open, online learning forums. Keywords: informal learning, social media, coding, content analysis, Reddit
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".