“The blackest shadows are cast by the Irish quarter”: the making of Stafford Street, Wolverhampton, 1832-1882
Bibliographic record
Abstract
This thesis seeks to understand the so-called Irish quarters that prompted speculation, condemnation and intervention in mid-Victorian cities. I analyse material and discursive constructions of the Irish quarter using spatial thinking and cross-disciplinary methodologies including GIS mapping and discourse analysis. Scholars of the Irish in Britain have typically studied the Irish as discrete and separate communities. Urban historians have studied the material and cultural construction of the Victorian slum. I draw these historiographies together by analysing the how the Irish quarter came to be: how the Irish were understood at local level, within contemporary understandings of race; how such neighbourhoods gained their notorious reputations; and how the self-perception of the Irish contributed to these spaces. To investigate this, I focus on the Stafford Street neighbourhood of Wolverhampton, synonymous with its Irish community in the mid-Victorian era. To outsiders, Stafford Street’s reputation was formed through territorial stigmatisation, religious discrimination and policing choices, viewed through lenses such as sanitary reform, criminal behaviour and anti-Catholicism, shaped by the power structures which formed Wulfrunian and British identities in the mid-nineteenth century. For residents however, everyday relationships and networks, supplemented by intertwining religious and political understandings of Irishness, formed a uniquely diasporic Irish space. I argue that rather than forming an Irish enclave or simply an imagined community, the Irish quarter was a very real diaspora space in which Irishness, Englishness, class, race and place were constantly revised and renegotiated. I argue too that space is a useful category of historical analysis for understanding marginalised communities, a means to access histories of those often deemed an inaccessible mass.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".