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Record W2572592180 · doi:10.32920/29936543

Demonstrations and the Law: Patterns of Law's Negative Effects on the Ground and the Practical Implications

2025· article· en· W2572592180 on OpenAlexaffabout
Basil Alexander

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Law and Evidence
Canadian institutionsQueen's UniversityUniversity of FrederictonUniversity of New Brunswick
Fundersnot available
KeywordsLawPolitical scienceEconomicsLaw and economics

Abstract

fetched live from OpenAlex

Although demonstrations are a recurring and key feature of Canadian and other societies, law often has negative and unacknowledged larger impacts on demonstrations while they occur when one examines how courts and police practically use and apply law on the ground. By pragmatically analyzing the experiences in Canada of Ipperwash, the Toronto G20, the Occupy movement, and “Idle No More,” this article illustrates patterns of how injunctions and criminal law processes negatively interacted with those demonstrations while they happened. The article begins by reviewing law’s usually detrimental impact on demonstrations-in-progress in the context of interlocutory and statutory injunctions (unless rare circumstances arise). For example, given the prior status quo focus of such injunctions, demonstrators have an uphill battle to practically win such motions. As well, using Hohfeldian conceptions, specific “rights” (such as property rights or regulated property use) usually prevail over more general aspirational “privileges” (such as freedom of expression and freedom of peaceful assembly) when they come into conflict. Law also does not usually act in prospective (or ex ante) manner for specific future or current situations. The article then examines how the police can use criminal powers and processes to effectively shut down or undermine demonstrations in the heat of the moment, notably because any after-the-fact (or ex post) reviews or accountability for misuse come much later, if at all. Finally, the conclusion explores some practical implications as a result and potential mitigation methods, such as more pragmatic understanding and balancing, better articulating some of the specific rights associated with demonstrations and dissent, and implementing more holistic and nuanced solutions, including trying to practically minimize and avoid raising tensions in such situations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.325

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0120.050
Scholarly communication0.0120.010
Open science0.0020.014
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.041
GPT teacher head0.377
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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