Bibliographic record
Abstract
This article focuses on three information-related puzzles related to how large law firms recruit entry-level lawyers. First, firms hire on the basis of limited information, usually restricted to two semesters of grades. Second, firms do not rely primarily on laterals, who are already trained. Third the recruiting process involves considerable redundancy, with both students and firms spending much time and energy talking with interlocutors they will not work with. The article first develops a descriptive narrative based on an empiri-cal study of recruiting in the Chicago legal market. The article then goes on to argue that each of the recruiting puzzles can be explained as a "two sided matching" problem, a common feature of labor mar-kets. In contrast with certain other matching markets (notably the market for medical residents), the market for law firm associates is decentralized, meaning that there is no mechanism to coordinate the participants in the market. The article speculates on why no cen-tralized matching mechanism for placing young associates exists. Why some professions utilize centralized matching mechanisms and others do not is a question that has not been addressed heretofore, despite a fairly well-developed literature on matching markets. Given that Canada has some experience with such a mechanism for placing young lawyers, to say nothing of the medical profession, the question is most certainly a relevant one with respect to the market for elite law firm associates.. The empirical study helps us under-stand the conditions under which a decentralized matching market will remain decentralized (or conversely, why a centralized matching market will centralize), a question not yet considered in the litera-ture.This article is relevant not only to the economics literature on match-ing markets, but also an addition to the literature on the sociology, economics and organization of the large law firm as a professional services firm. Despite a fairly broad literature on how law firms maintain, motivate and jettison their members, there has been little attention to how large law firms select these members in the first place.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".