Mapping online visuals of shale gas controversy: a digital methods approach
Bibliographic record
Abstract
The internet is an increasingly influential actor and arena for debating emerging sustainability controversies, but studies often overlook the role of visualisations in online spreading of information. This paper offers a way to better understand this role: what images do competing online actors use, are there differences between opponents and proponents, differences between internet regions, and are there shifts in their online visualisations over time? Adopting a controversy studies perspective and the digital methods approach, we studied the online spread of visual information. We compared the use of visualisation about shale gas on top-ranked pages in the internet regions of South Africa, Mexico and the United Kingdom in 2018 and 2019. The results indicate a connection between the actor’s standpoints in the controversy and the type of image used. In Mexico, proponents and neutrals used, most of all, photographs of people (officials). Opponents posted more data visuals. South African and British neutral actors used more data visuals, while proponents posted landscapes and opponents photographs of people (protesters). Also, we noticed that changes in the actor’s position in the controversy between 2018 and 2019 coincided with changes in the use of type and content of visualisations. Context-specifics of each country offered possible explanations for these shifts in standpoint and visualisation of the controversy. Our study indicates that visuals are highly relevant digital objects in public debate and the decision-making process.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".