When the attributive becomes relational. A look at the innovative sector in Argentina based on regional customer and supplier networks by activity branch (2012-2018)
Bibliographic record
Abstract
This paper compares the relations between certain branches and companies of the innovative sector in Argentina with the regions of the map with which they maintain commercial ties. Data from the National Survey on the Dynamics of Employment and Innovation (ENDEI- MINCyT and MTEySS) were used, which contain anonymous information on innovative companies. From these data, relational matrices were constructed in 2-modes between nodes (companies), suppliers and clients (at the regional level), segmented by company size and activity branches. We focused on identifying differences and similarities according to the company's activity branch and size and the composition of regional exchanges between clients and suppliers for the periods 2010-2012 and 2016-2018, which corresponded to the ENDEI I and II bases. 2-mode networks were built, linking companies with the regions where they have customers and suppliers, resulting in multiplexed bimodal networks. Specific branches were selected, and one-mode networks were built, obtaining symmetric matrices from the technique of co-occurrence and affiliation. Cohesion and centrality calculations yielded higher density for customer networks than supplier networks in food sectors. Regarding centrality, a higher nodal degree of exports was observed for the Mercosur region and the rest of Latin America and, to a lesser extent, for the regions of the northern hemisphere (Europe, USA and Canada) and Asia, Africa, and Oceania. Regarding the nodal degree and density, a drop was observed between periods when comparing the two surveys, except for clients in the case of the pharmaceutical industry, showing an improvement for the Mercosur, Latin America and Asia regions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".