Slovenska podjetniška demografija in prenos podjetij
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Slovenian Enterprise Demography and Business Transfer: Slovenian Entrepreneurship Observatory 2018. In this monograph, we analysed all companies and entrepreneurs in Slovenia for the year 2017, then we compared for the year 2016 or 2015 key data of Slovenia and EU-28 or individual member states in the non-financial business economy. In Slovenia, in 2017 122,618 businesses employed 563,356 people. The majority of businesses (nearly one fifth) operated in the wholesale and retail trade; maintenance and repair of motor vehicles. Likewise, in the EU-28 in 2016 more than a quarter of businesses (25.9% or 6.3 million) was active in the wholesale and retail trade; maintenance and repair of motor vehicles. The average value added per person employed for the aggregated activities of the EU-28 in the year 2016 amounted to 50,900 euros, while in Slovenia 32,700 euros (36% less). In the second part, we studied the transfer of companies. We surveyed a selected sample of experts who represent important actors in a supportive environment for SME transfers. We also carried out a survey on the transfer of companies among the founders/owners of SMEs in Slovenia. We were interested in what type of support they need in this process. In this, we limited ourselves to the age group of entrepreneurs 55+.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 it