Analysis of Subcontracting in the Construction Industry in Indonesia
Bibliographic record
Abstract
This paper presents an empirical analysis and justification on collaboration between small and large enterprises through strengthening partnership (subcontracting) in order to strengthen the contribution of the construction sector in the national economy. Some conclusions that can be taken that: (i) the construction of market share inequality can be balanced with application the partnership (subcontracting); (ii) Siding with non-small enterprises/large (mostly foreign entities) will push the capital outflows and reduce the competitiveness of the nation; (iii) Strengthening the supply chain through partnership scenario (subcontracting) on the entire flow of the chain will support the role of small business concerns in seizing the national construction market; (iv) weakness of collaboration patterns can be reduced by setting policies that favor (affirmative) the existence of a small body as part of the construction industry; (v) The construction market continues to increase is not offset by an increase in the construction market of small business entities resulting gap portion of the acquisition capitalization construction. (vi) Policy subcontracting by small business entities will potentially increase the absorption of the construction workforce (value added) and construction GDP in national GDP and consequently increase economic growth as an indicator of the competitiveness of nations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".