Doing Contract Theory in First Year Law School: The Iceberg Method
Bibliographic record
Abstract
There are various ways of introducing first year law students to legal theory. One is to offer a discrete legal theory course. This is a "bottom-up" approach in the sense that typically discussion of a given theory in the abstract precedes application of the theory to particular legal problems. An alternative is to teach theory as part and parcel of one or more of the standard first year courses. This is a "top-down " approach in the sense that identification of the legal problem precedes the introduction of potential theoretical solutions. The top-down approach can be visualized as an iceberg, with particular legal rules at the peak and the policy preferences which shape the rules submerged below. In this paper, we explore a variation of the top-down or iceberg approach in which the aim is to introduce students to a range of theoretical perspectives by comparing and contrasting the application of competing legal theories to a particular legal problem. For the purposes of illustration, we have chosen the spousal guarantee cases in Contract Law as our reference point and critical legal studies, law and economics and feminist theory as our perspectives. There are numerous other cases, not just in Contracts, but in Torts, Property and Criminal Law as well, that might be amenable to similar treatment. Likewise, needless to say, there are other theories, apart from the ones we use here, that teachers might prefer; the teaching method we propose does not depend on the particular perspectives we happen to have chosen
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".