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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".