The “Hairs of Hope”: Toward a Fuller Understanding of the Legal, Material, and Social Infrastructure of Infrastructure
Bibliographic record
Abstract
Abstract The article has the main aim of utilizing the literature on “fragment urbanism” and case studies in infrastructure from the global South to question the notion—dear to the World Bank and the IMF—that the global South ought to follow the North’s lead in aiming at “the modern infrastructure ideal,” that is, a series of integrated nation-wide networks. That model suits certain needs—electricity, phone service, perhaps Internet—but it doesn’t always work, even if funding can be found, for many other infrastructure needs. What is often thought of as “informal” solutions may in fact deploy more site-specific and community-specific techniques and tools. The article also shows that even in the global North’s most advanced capitalist countries, the lack of overall planning and the absence of needs assessments done before choosing which projects will go ahead mean that infrastructure provision and governance is far more fragmented than the “modern” ideal would suggest. The fact that major projects are usually financed separately, often having their own credit rating, encourages a way of non-evidence based planning that is rife for political interference in infrastructure decision-making. The “art of the deal” is in fact the model for infrastructure projects these days, not the ‘seeing like a state’ that characterized many projects in the post-World War II era.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.116 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".