A Massive Open Online Course on climate change: the social construction of a global problem using new tools for connectedness
Bibliographic record
Abstract
Climate change is a pervasive and challenging phenomenon that takes on a variety of meanings and frames, each of which suggests different victims, villains, and solutions. New tools are emerging that may facilitate a reframing, or at least the collaborative coproduction, of the climate change conversation. Web‐based social media have provided a new level of connectedness and capacity to collaborate through a merging of the social and educational worlds in the form of Massive Open Online Courses (MOOCs): web‐based, freely available courses taught by university and college instructors, and offered to thousands of students at a time. Our development and delivery of the first interdisciplinary climate changeMOOChas opened a new window into (1) the tools available to convene a conversation about climate change, (2) the processes of negotiation, cultural articulation, and identity formation that occur through conversations that include large populations from diverse backgrounds, and (3) the implications of this conversation for the broader climate change discourse, the definition of the problem, attributions of responsibility, and the development of solutions.WIREs Clim Change2014, 5:577–585. doi: 10.1002/wcc.300 This article is categorized under: Perceptions, Behavior, and Communication of Climate Change > Communication Social Status of Climate Change Knowledge > Climate Science and Social Movements
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".