Being-in-the-City: A Phenomenological Approach to Technological Experience
Bibliographic record
Abstract
This paper examines dynamics surrounding the negotiation and articulation of the body-technology relationship necessarily characterizing the experience of being-in-the-city. Nowhere is everyday experience more mediated by technology than in the city. Being-in-the-city involves being embodied by technology at levels ranging from micro to macro. Despite the fact that technologies are constantly evolving in city space, relations with technology tend to become quickly normalized — mundane — transparent. Given this normalization as well as the sheer pervasiveness of technology in constituting city space it is important to examine the ways in which technology comes to shape the experiential contexts of everyday life. In urban space, technologies result is new sights to be seen, sounds to be heard, smells to be smelt, textures to be felt, as well as altogether new modes of experiencing the everyday. In exploring the dynamics surrounding the ongoing, multi-layered negotiation and articulation of the body-technology relationship necessarily characterizing the experience of being-in-the-city a phenomenological perspective is adopted. Heidegger’s writing on technology, Merleau-Ponty’s writing on embodiment and perception, and Don Ihde’s writing on the body and technology contribute to a theoretical framework for a phenomenological examination of the experiential implications of being-in-the-city, a technological ecology.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".