Shifting into “Neutral”: Evaluating Mediation as a Peaceful Alternative to the Forceful Resolution of the 2022 Canada–Freedom Convoy Dispute
Bibliographic record
Abstract
In early 2022, the Canadian government found itself confronted by a group of truck drivers—in what came to be known as the “Freedom Convoy”—protesting government-imposed restrictions related to the COVID-19 pandemic. This article evaluates how mediation could—and should—have been used as an effective means for the government and protestors to resolve their dispute. It begins by defining the government health and safety measures that prompted the protests and describing the ensuing protest movement by the Freedom Convoy. The article then discusses the protest’s implications on commerce and on the communities where it was located. Next, the article describes the unilateral approach the Canadian government took to shutting the protests down by force. The article then explores some of the general benefits of mediation as a dispute resolution process, and argues that mediation could have been used to avoid some of the consequences of the Canadian government’s retaliation to the Freedom Convoy protest. To demonstrate the potential benefits of the proposed mediation, that such a mediation could have been successful, and that a procedure exists that an appointed mediator could have followed, the article compares the proposed mediation to two well-established types of mediation: public policy mediation and labor–management mediation. The article goes on to address counterarguments by acknowledging some potential benefits of the government’s unilateral approach, but maintains that mediation could have achieved those benefits as well, particularly an expedient return to the flow of commerce. Finally, the article pushes back on the argument that a “failed mediation” would have been a waste of time, as even mediations with no agreements can bring some satisfaction to the parties involved. The article does not attempt to advocate for what the results of such a mediation should have been.
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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 | 0.051 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".