Are inner‐cities bad for your health? Comparisons of residents’ and third parties’ perceptions of the urban neighbourhood of Gospel Oak, London
Bibliographic record
Abstract
This paper analyses representations of the neighbourhood of Gospel Oak (London, UK), by contrasting views of residents with views expressed by third parties. Data from residents were gathered through in-depth qualitative methods. Data from third parties were gathered through documentary analysis. Third parties' descriptions of Gospel Oak were significantly more negative than residents'. In contrast, residents were overwhelmingly positive about the neighbourhood, often taking a diametrically opposed view to third parties on the same factor, for example, quality of housing. We argue that third parties' negative social construction of Gospel Oak is functional rather than descriptive; a pathological orientation is usually taken to assist efforts to win regeneration funding. Though this is sometimes successful, we discuss possible negative affects of this social construction, for example, stigmatisation. Finally, we warn against making assumptions of collective social and physical pathology in urban neighbourhoods, urging a more critical approach to the study of the inner-city in the health sciences.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".