Missing targets: Engagement metrics and digital organizing in the climate movement
Bibliographic record
Abstract
Despite limitations and uncertainties, digital media platforms are integral to mobilization and organizing in the climate movement. Their appeal and utility for public engagement is largely attributed to direct interactions among users, increased visibility, and the ability to measure and validate these interactions through quantified engagement metrics. While the affordances of specific platforms and their influence on social movements have been extensively studied in existing scholarship, the relationship between engagement metrics and climate activism requires further attention. Therefore, this article focuses on the relationship between ubiquitous engagement metrics on digital media platforms and digital organizers in the climate movement. It highlights the different kinds of internal and external stakes for digital climate activists as well as the challenges and compromises that occur when platform affordances – especially their tendency to flatten and quantify interactions – come to be entwined with organizing. The article suggests that future scholarship needs to look beyond perspectives that exclusively emphasize either the technical hostility of platforms or the interpretative flexibility of users that currently define scholarly understanding of the relationship between metrics and users. This can be achieved by paying greater attention to sociopolitical conditions, such as internet access, regulatory frameworks and national climate politics that influence the experiences of digital organizers in the climate movement. These insights can support strategies relevant to different regional, technical and temporal constraints that are so crucial to achieving effective climate action.
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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 | 0.022 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".