Mobilization and Latency Dynamics in the #StopLine3 Discourse
Bibliographic record
Abstract
After the Canadian oil corporation Enbridge proposed replacing its Line 3 pipeline in 2014, activists began protesting against its environmental risks and violations of Indigenous rights, among other concerns. As the pipeline’s construction progressed and resistance intensified, a parallel discourse emerged online under the hashtag #StopLine3. This study explores the temporal evolution of that discourse and its alignment with on-the-ground developments. Specifically, we assess whether the discourse conforms to a phasic model of collective action inspired by Melucci, which contends that social movements oscillate between phases of visibility and latency. This oscillation is mediated by key mobilizing events, which drive activists to focus their energy toward fighting a target. Once a resolution is reached (positive or negative), the movement progresses into a latent phase, where participants regroup, reflect, and build unity. The ideas behind this model were developed before the digital turn. Given how social media complicates temporality, it remains unclear how well the model explains discursive resistance online. We use #StopLine3 as a case study to test the phasic model, examining all tweets with the hashtag posted between 2016 and 2023. We break up the data into temporal segments based on peaks and lulls in overall activity and explore how both tweet content and forms of engagement shift from segment to segment. In line with the model, we find that the shifts between segments are mediated by key events; however, we also find that the Twitter discourse consistently favors mobilization-oriented forms of engagement and content over latency. Our results suggest that Twitter primarily facilitates mobilization work, and call into question the importance of latency work, what it looks like, and where it takes place on platforms such as Twitter. We argue that Twitter may not be an effective venue for latency processes, or alternatively, may alter how those processes manifest. Overall, we trouble the application of the phasic model to #StopLine3 and other similar public-facing discourses.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".