An Investigation of Industry Associations, Association Loops, and Economic Complexity: Application to Canada and the United States
Bibliographic record
Abstract
Various methods were proposed to understand the linkages in an input- output system; however many focused only on the identification of key sectors in the economy. An alternative approach, identifying analytically importance of elements and combinations of elements was proposed as a field of influence theory (Sonis et al., 1996). The purpose of this paper is to offer a complementary approach to the field of influence and the socalled 'Matrioshka principal' (Sonis and Hewings, 1990); the objectives are to identify simple row-column associations (i.e. statistical dependence), seek hierarchical associations between supply and demand in input-output systems and the decomposition of economic complexity into finite stages. For the identification of simple dependencies between rows and columns, we use a log-linear regression and for hierarchical associations and the identification of complexity stages, we use the data analysis technique known as dual scaling. Results of both approaches will be applied to input-output tables of the US and Canada.
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.
How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".