Discovering the Best Criminology Program in Poland: Contemplation of the Month-long Sabbatical at the University of Białystok
Bibliographic record
Abstract
This article traces the friendship between Professor Emil Pływaczewski and me over a quarter of a century with an emphasis on my impressions of Poland in general and Białystok School of Criminology in particular during my recent one-month stay. While I have been fascinated by the best criminology program growing from none to the current prominence, I argue that criminology’s potential as avant-garde of legal reform before the passage of law and as evidence-based evaluation has not been fully developed in Poland. International criminology as a method permeates every aspect of research. As a progressive and meliorative major, criminology could further promote good and inclusive society and play a role in closing the gap between the survivalist culture and self-expressionist culture by strengthening justice-based institutional structure and the rule of law through ramping-up global connectivity among international scholars.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".