The Science and Politics of Infrastructure Research: Asserting Power, Place, and Agency in Infrastructure Knowledge
Bibliographic record
Abstract
Despite over half a century of research concerning infrastructure development processes, structurally oriented development theories continue to dominate infrastructure research and praxis. Critically informed approaches to development, which acknowledge the integral role of power, place, and agency to infrastructure research, have yet to make a noticeable mark within infrastructure development policy-making. Towards the goal of giving greater prominence to the critical perspective, I propose the Critical Acquisition Framework. The framework is designed to facilitate an agency-oriented understanding of infrastructure development processes from the perspectives of marginalized groups. Inspired by critical-social theory and capability analyses, the Critical Acquisition Framework helps to understand how marginalized groups deploy their existing capability sets to access infrastructure via multiple and overlapping institutions. In addition, the framework helps to envision alternative agency-oriented scenarios of infrastructure access. In essence, the framework demonstrates how the acquisition process influences the capability sets and therefore power of marginalized groups, and can be used to assess whether infrastructure “develops” according to local perspectives, or reifies inequitable power relations.
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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 | 0.035 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.012 | 0.179 |
| Scholarly communication | 0.023 | 0.034 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".