Organizational Change In The Legal Education Environment: Institutional And Individual Responses To Times Of Crisis
Bibliographic record
Abstract
The Great Recession of 2008 brought great upheaval to many aspects of the American economy. At first law schools saw an increase in applications and enrollment as individuals sought an education that would lead to employment. Within a few years, however, the job market for new lawyers deteriorated. By 2010-2011, the number of applications to law schools plummeted, as did the enrollment numbers. Since tuition is the life blood of law schools, the field of legal education was faced with an unprecedented crisis. This researcher collected and reviewed publicly available data to examine the changes that occurred in law schools following the economic downturn. Interviews with faculty who had experienced the institutional changes portrayed the personal or internal changes that occurred as a result. Findings suggest that higher-ranked (Tier 1 and Tier 2) schools generally had different outcomes than lower-ranked (Tier 3 and Tier 4) schools following the enrollment crisis. The first section addressed structural changes, the second section addressed programmatic changes, and the third section reported on faculty in the Tier 3 and Tier 4 schools expressed feelings of grief and loss regarding their experiences during this period of crisis. The future loss of accreditation that may occur in some law schools will be a source of additional study of institutional and personal grief and loss issues that schools and faculty members experience as a result of that loss.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".