A Framework for Short- vs. Long-Term Risk Indicators for Outsourcing Potential for Enterprises Participating in Global Value Chains: Evidence from Western Balkan Countries
Bibliographic record
Abstract
This paper aims to present a benchmarking framework for short- and long-term risk of enterprises in emerging markets that seek integration in global value chains. The benchmark instrument aims in particular to assess short- and long-term risk based on accounting data and estimations of key financial ratios for enterprises located in the Western Balkan region and operating in the materials, industrials, and customer-discretionary industries. In total, the paper considers 310 enterprises. Given the geographical proximity of the region, the benchmark instrument for short- and long-term risks serves to assess the outsourcing potential these enterprises have toward foreign enterprises dominating larger markets such as the European value chain. The framework is applicable to a large-scale annual data series collected on subindustry level in order to obtain a more granular analysis of a particular industry and its respective value chain. The benchmarking instrument indicates that those subindustries performing better both at short- and long-term risk display a higher outsourcing potential and more opportunities for integration in global value chains.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".