Alliances in networks: insights from blockmodeling
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
Purpose – Economic agents in systems (individuals, firms, government organizations, etc.) engage in a wide range of cooperative activities that may be mapped as networks. This paper aims at determining whether alliances embedded in such networks show higher densities of interaction between agents than other network subsets. Design/methodology/approach – This paper uses the blockmodeling technique on a unique sample of armed forces that have engaged in repeated cooperative behaviour over a decade. Findings – This study finds that the alliance in the sample does exhibit a significantly higher density of interaction than the rest of the network. Research limitations/implications – Using blockmodeling may be necessary, but not sufficient, to ascertain the presence of undisclosed alliances in networks. Practical implications – This work is useful for the detection of potential or actual collusive behaviour in the form of higher densities of interactions between agents in systems. Originality/value – Blockmodeling, as a technique, and agents like armed forces, as a sample, are uncommon occurrences in the contemporary cybernetics and general systems literature. This paper provides novel insights to research on collaborative behaviour.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it