Bibliographic record
Abstract
Over the last several decades, courts and legal scholars have struggled with whether or when to consider boilerplate text as contract. Recent attempts to draw all boilerplate text into “contract” seek to end that struggle but have shifted contract law away from its traditional focus on enforcing parties’ actual agreements and common understandings. This has required a series of ad hoc “fixes” to contract law reminiscent of the medieval use of “epicycles” to try to square geocentric theories of planetary motion with recalcitrant observations of a nongeocentric universe. This shift has been transforming the meanings of contract law’s central concepts. We view the shift as an undiagnosed paradigm slip, resulting in a generalized theory of “contract” as a mere assumption of risk that allows private obligations to be created unilaterally without reaching the actual agreements required by core contract law principles. Some now call this new sort of obligation “contract.” But it is pseudo-contract, resembling contract without fulfilling its necessary conditions of validity. The recent paradigm slip into pseudo-contract raises a complex blend of linguistic, factual, conceptual, practical, normative, and doctrinal problems. Under the mantle of “contract,” the problems of pseudo-contract have remained largely hidden. In this Article we expose these problems and develop a more nuanced and coherent method of analysis — shared meaning analysis — that courts and other legal analysts can use to determine when any particular piece of boilerplate text does, or does not, contribute an actual term to a contract. Because facts about language have received insufficient attention in discussions of how boilerplate text may (or may not) contribute to contract meaning, we launch our analysis by developing several seminal insights into the dependence of meaning on social cooperation from the language philosopher Paul Grice. Drawing on his insights into language, we develop a contemporary definition of the shared meaning of a contract (or the “common meaning of the parties”) as that meaning that is most consistent with the presupposition that both parties were using language cooperatively to contract. We then offer a simple conceptual test that courts can use to discern this shared meaning, distinguish contractual from noncontractual uses of boilerplate text, and prevent contract from slipping into pseudo-contract. We pay particular attention to diagnosing deceptive or misleading uses of boilerplate text. Using examples ranging widely from clickwrap consumer contracts to high-end boilerplate contracts between sophisticated parties, we show how shared meaning analysis applies generally to many varieties of contract.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.009 | 0.024 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".