Teaching contracts backwards: benefits of starting with remedies in contract law pedagogy
Bibliographic record
Abstract
This paper advances the thesis that beginning contract law instruction with remedies offers significant pedagogical advantages. While most contract law courses follow a traditional sequence beginning with formation, this paper claims that starting with remedies provides students with a more concrete foundation for understanding contract law’s theoretical underpinnings. The paper traces the historical development of contract law pedagogy from its apprenticeship origins and examines how the traditional formation-first sequence became entrenched in legal education through will theories of contracts in the nineteenth century. The paper demonstrates how a remedies-first approach can better illuminate core theoretical principles and competing frameworks in contract law. While acknowledging that different pedagogical approaches may suit different teaching objectives, this paper specifically addresses theory-oriented educators, showing how beginning with remedies naturally leads students towards deeper theoretical insights about contractual obligations and their enforcement. The paper contributes to ongoing debates about legal pedagogy by offering both theoretical justification and practical strategies for implementing a remedies-first approach in contract law instruction. Through this analysis, the paper aims to enrich pedagogical discourse and provide new tools for helping students develop a more sophisticated understanding of contract law’s theoretical foundations.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".