Infrastructure Fieldnotes: Engaging the City through Reading, Research, and Representations
Bibliographic record
Abstract
As part of a recent undergraduate seminar on infrastructure, students completed weekly exercises dubbed “infrastructure fieldnotes.” Going beyond conventional discussion board posts or reading responses, exercise prompts incorporated reading analysis, methods practice, writing prompts, and experiments in multimodal representation as students engaged with urban planning and quotidian experiences of infrastructure and made sense of the infrastructures that enable and structure city life. In this research article, the instructor for the course offers a preliminary presentation of the assignment’s structure and pedagogical objectives, followed by an analysis of how some prompts influenced classroom discussions by creating common points of reference and revealing different experiences of the campus and city. This discussion is followed by five student contributions on different aspects of the assignment. Some take up specific prompts to demonstrate how they created openings for engagement with course material, some reflect on how exercises enabled students to cultivate new kinds of awareness or attention to infrastructure, and others extend the fieldnotes project beyond the class to show what kinds of analysis endured after the course ended. Altogether, these student analyses demonstrate and reflect on the utility of sustained, open-ended prompts for student engagement with course material and concepts in an urban campus.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".