Bibliographic record
Abstract
Hrvatska udruga socijalnih pedagoga SAZETAK Cilj je ovog, rada pregled zakonskih propisa Rcpublike Hn'atske, Europskih z.emalja, Rus{je i z.emalja Sjevenrcameritkog kontinenta (SAD-a i Kanade) te poredha navedenih.Izloiena miilje- nja i pokreti u navedenim zemljama logitno vode do nag,laska na stavove za i protiv dekriminali- zacije i legalizacije ilegalnih droga u Republici Hrvatskoj i svijetu.Rad ukazuje na kompleks- nost cjelokupne problematike koja je trenutno vrlo aktualna kako u RH tqko i u svijetu.Moi.e se zakljutiti da interes vlade pojedine driat'e te njezin utjecaj na ulu i iiru druitvenu zajednicu putem javnih medija odreduje smjernice driave (RH nije izuzetak) premo dekriminalizaciji i legalizaciji odredenih vrsta ilegalnih droga.Klj uE ne rij e E i : de kriminalizac ij a, le g al izac ij a, i I e g alne d ro g e 1. UVOD Droge su oduv|ek zanimale dovjeka koji je istraZivao njihova svojstva i na taj ih je nadin po- ku5ao iskoristiti kao pomoi u mnogim sferama Zi- vota.Danas se susreiemo s mi5ljenjima, stavovima i burnim raspravama o njezinoj dekriminalizaciji i legal izacij i. Dekriminal i zacija znali da j e posjedo- vanje odredenih zakonom propisanih droga i njihove zakonom propisane kolidine za osobnu upotrebu prekr5aj, a ne kazneno djelo.Pod pojmom legalizacije misli se da posjedovanje odredenih droga i njihove propisane kolidine za osobnu upotrebu nije niti kazneno djelo niti prekr5aj.Postavljaju se brojna pitanja o vrlo aktualnoj temi.Dok jedna struja smatra da bi dekriminalizacija i legalizacija rije5ila probleme crnog trZi5ta, organiziranog kriminaliteta, te gubitak ogromnih sredstava drtave koja se koriste u borbi protiv narkomafije, druga struja smatra da se rat protiv droga mora nastaviti na na- din da se koriste (ili ne biraju?) sredstva koja imaju za konadni cilj smanjiti dostupnost droga na ilegalnom trZi5tu.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".