Temporalities of infrastructure: An ethnographic study of rural road building, spectral mining, and good living
Bibliographic record
Abstract
Purpose We examine how citizens and the state negotiate infrastructure projects using the concept of temporal incongruence. We consider infrastructures in a plural sense, following several projects in Andean, Ecuador, a region known for challenging roads and a history mining exploration and resistance. We set these layered infrastructure projects in the context of the postneoliberal state, an era characterized by the building of unprecedented mega-infrastructure as a way of fostering human wellbeing. Findings The introduction of the political value, buen vivir (good living) set the stage for the shift in infrastructure temporality by creating a political and economic environment that prioritizes human and ecological well-being. By juxtaposing the state-led approach to infrastructure with people's responses, we tease out temporal incongruence. We found that people's responses to infrastructure projects are closely tied to their ability to meet their immediate needs and ensure the well-being of people and nature. Conclusion Temporal incongruence is not merely a mismatch of timelines but a site of political contestation, where competing visions of development are negotiated and reimagined. The temporal reorientation towards the present, via buen vivir, calls for scholarly attention to the immediacy of lived experience taking precedence over yet to be delivered future gains.
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.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".