Enjoyable life: Planning, amenity and the contested terrain of urban biopolitics
Bibliographic record
Abstract
This article explores the connections between urban planning and a particular form of biopolitics. These connections are investigated by looking at the emergence of “enjoyment” as a planning concern in late 1960s Halifax, Nova Scotia. This new concern, the article suggests, emerged as a result of a political struggle involving activist groups, a newly formed state agency, and elements of the post-World War II political establishment. Wedded to this concern were two essential planning policies: the promotion of “amenity” (especially in the downtown) and the introduction of structured “citizen involvement” in planning decisions. Together, these two policies inaugurated a new form of planning and biopolitics. The promotion of amenity aimed to create a more enjoyable life through the alteration of prevailing conditions of life, while citizen involvement routed planning decisions – including the precise meaning of amenity – through “liberal” practices of government. Most importantly, the new policies were shaped by the enactment of normative divisions within the population, a characteristically biopolitical effect. The result of these divisions was a highly unequal process of citizen involvement and a correspondingly uneven terrain of enjoyment: a terrain whose development and use would provide enjoyment for “normative” populations, while leaving “pathological” populations unaffected or worse.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".