Mobilization capacity: Tracing the path from having networks to capturing resources
Bibliographic record
Abstract
A key puzzle in social network research is why people have networks in theory but fail to extract resources from them in practice. We propose the concept of mobilization capacity— one’s efficiency in extracting resources from networks—to help explain this gap. Mobilization capacity involves several critical microprocesses that account for what often appears as error in network models, given that having a network structure does not precisely translate into attaining outcomes. The determinants of mobilization capacity arise at actor- and relational- levels. Actor-level determinants include the actor’s willingness to seek network resources and ability to accurately locate network resources. Relational determinants involve cooperative intent in the relationship and the ability to successfully exchange resources within that interaction. Using these dimensions, we consider how actors realize or degrade their structural potential as they attempt to capture value from their networks. We conclude with an illustrative example of the Matthew effect by describing how each component of mobilization capacity compounds structural advantage, with the structurally rich enjoying efficiencies in resource extraction and the structurally poor further disadvantaged, which increases inequality.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.026 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".