Topology and Dependency Tests in Spatial and Network Autoregressive Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social network analysis has been identified as a promising direction for further applications of spatial statistical and econometric models. The type of network analysis envisioned is formally identical to the analysis of geographical systems, in that both involve the measurement of dependence between observations connected by edges that constitute a system. An important item, which has not been investigated in this context, is the potential relationship between the topology properties of networks (or network descriptions of geographical systems) and the properties of spatial models and tests. The objective of this article is to investigate, within a simulation setting, the ability of spatial dependency tests to identify a spatial/network autoregressive model when two network topology measures, namely degree distribution and clustering, are controlled. Drawing on a large data set of synthetically controlled social networks, the impact of network topology on dependency tests is investigated under a hierarchy of topology factors, sample size, and autocorrelation strength. In addition, topology factors are related to known properties of empirical systems.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it