Social network analysis, Markov Chains and input-output models: combining tools to map and measure the circulation of currency in small economies.
Bibliographic record
Abstract
Localization is posited as the antidote for globalization, but little exists in the way of quantifying the micro-scale of small communities. In this paper, the reader's attention is drawn towards understanding that social network analysis, Markov Chains and input-output models are equivalent, and that together these tools can be used to and measure the circulation of currency in a small community. The map of the economy can be created using social network analysis, in a form equivalent to Markov Chains and input-output models, by representing businesses as nodes and the percentage of expenses spent by one business at another as the strength of the edges between the nodes. Markov Chain mathematics is used to measure the circulation of currency in the economy by calculating the average number of transactions (average path length) from where the dollar enters the community until it leaves. This method is equivalent to the multiplier effect from input-output models but more granular. An example using a simple loop is provided showing different methods of solutions, their equivalence and the impact of loops on the average number of transactions in a small economy.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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 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".