A Framework for Interpreting Emojis in Legal Contexts
Bibliographic record
Abstract
In the 2023 case of South West Terminal Ltd. v. Achter Land & Cattle Ltd., a Saskatchewan court found that a thumbs-up emoji, as a standalone item of communication, constituted the acceptance of a contract between a buyer and seller. The trial judge noted that such communication was “the new reality in Canadian society” for which courts should be prepared to interpret novel units of language arising in the digital age. However, an analysis of recent Canadian cases involving emojis shows that courts have not been prepared, with inconsistencies in how emojis are represented in evidence, how they are analysed, how much interpretive weight they are given, or whether they are dismissed as decorative and without linguistic value. This paper argues that while emojis are not a standardized form of communication, they hold linguistic value which makes them critical to the interpretation of evidence. Part I reviews how the field of linguistics has studied emojis. Part II explains how a corpus of English-language Canadian case law was built and analysed to map patterns and inconsistencies in Canadian courts’ emoji interpretation. It also argues that the South West analysis of the emoji in question provides a skeleton for an interpretive framework for emoji. Part III outlines how such a framework could be realized, drawing from both linguistics and jurisprudence. The paper concludes with a caution against courts’ downplaying the communicative value and function of emojis and argues that a structured interpretive approach could help courts more accurately infer meaning from typed communication in evidence.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.014 | 0.055 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".