Bibliographic record
Abstract
Okolina se sastoji od elemenata koji utjecu na poslovanje poduzeca, a koje menadžment mora uvažavati pri donosenju odluka, možemo reci da se okolina poduzeca tretira kao set svih vanjskih i unutarnjih faktora koji mogu utjecati na putu poduzeca prema ostvarivanju svojih ciljeva. Kad utjecaj okoline postane dominantan u odnosu prema utjecaju poduzeca na tu okolinu, tada poduzece zapada u krizu iz koje tesko može izici, upravo iz tog razloga u ovom radu bavimo se analizom okoline provedenom na primjeru poduzeca Metis d.d., ciji je temeljni zadatak prije svega ustanoviti prilike i prijetnje u vanjskoj okolini te snage i slabosti u unutarnjoj okolini poduzeca iz cega proizlazi ocjena okoline koja treba poslužiti menadžmentu za brzo reagiranje, a samim time osigurati povecanje izgleda za uspjeh poduzeca na tržistu. PEST analizira okolinu, za tržiste u nastajanju ili vec postojece i pruža pregled vanjske situacije koja može imati utjecaj na industriju u globalu ili na poduzeca unutar promatrane industrije. Pomocu SWOT analize identificiraju se kljucni cimbenici poduzeca. Namijenjena je vrednovanju usklađenosti sposobnosti poduzeca s uvjetima u okolini poduzeca. U radu je provedena analiza vanjske i unutarnje okoline poduzeca Metis d.d. te je izrađena SWOT analiza za navedeno poduzece i oblikovanje strategije.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.126 | 0.028 |
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".