Mending the Net: Public Strategies for the Remediation of Network Failures
Bibliographic record
Abstract
Abstract Market and hierarchical/organizational failures have long been the target of public policies explicitly aimed to mitigate their negative effects. However, in spite of a growing interest in policies around industrial clusters and business networks, scholarship on public efforts at remediating network failures has been ad hoc and lacking a binding theory. A central question is what strategies public agencies employ to repair network failures. We begin to answer this question by distinguishing between two distinct approaches: (1) “network construction” in which government agents actively build, re-shape, or thicken the structures of private sector networks; and (2) “network activation” in which government agents seek to alter the internal dynamics of existing private sector networks. To provide empirical support for these concepts, we provide a series of short international examples to illustrate the scope of network remediation activities as well as two in-depth cases that demonstrate how these mechanisms can work: the Canadian Industrial Research Assistance Program (IRAP) and the specialized Mexican Lead Substitution Program.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".