McLuhan's Mid-Century Urbanism Now: Creating Sensory Environments with Machinic and Computational Technologies
Bibliographic record
Abstract
As computational media has receded from the forefront of urban imaginaries, there has been a swing to promoting ethical and cultural values and eco-consciousness, as if these are divided claims. Putting the two together, this essay argues for acknowledging the role of media in urban environments so that it can be adjusted to enhance life and embellish relational networks. Mid-century, Marshall McLuhan saw media as having overtaken nature, anticipating what many contemporary scholars refer to as the Anthropocene. For him, the death of nature meant it was time to address questions of how best to monitor and deliver media, especially in urban landscape structures that need to be designed to stimulate sentience and enable new forms of citizen involvement. He warned against accelerant technologies built to serve government and corporate interests and advised seeking adaptations to suit local environments and human needs. His relational and ecological view assumed that innovation had intersectional environmental impacts: changing any one thing meant changing everything. McLuhan's theory and strategies push back against paternalistic stewardship in calling for urban structures to be arranged to stimulate sentience and designed for citizen involvement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".