Akademicy Millennialsi, studenci Gen Z: Jak zmiana pokoleniowa wpłynie na edukację prawniczą
Bibliographic record
Abstract
The past two decades saw a generational change come to the universities along with the technological one: the very first digital natives, the Millennials, arrived. Gen Z soon followed. The purpose of this paper is to analyse the two somewhat similar, but often different generations and place them within the context of the Polish and North American university, the law faculty in particular, in order to answer the question: What does this shift of generations mean for the future of legal education? In the first part of the paper the author introduces the two generations, contrasting them with the previous ones. The second part of the paper is devoted to the issue of Millennials and Gen Z at the university, particularly in law school. In the final part of the paper the author applies the findings of two previous sections to the question of the future of legal education. Arguing that law faculties are unique entities within the university, he proposes a number of changes to the teaching of law which should be introduced if Millennials and Gen Zs are to truly find their place in the academia and be able to live up to their full potential as lawyers, be that practitioners or academics.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".