A Comparative Approach to Identifying the Irish in Long Eighteenth-Century London

Crymble, Adam

doi:10.1080/01615440.2015.1007194

Cited by 5 publications

(6 citation statements)

References 37 publications

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“…71 To bring us closer to a solution to this problem, this study adopted a three-pronged approach to identifying Irish defendants, outlined in detail in [a previous paper by the author], which draws upon the best practices used by King, but seeks also to identify members of the wider ethnically Irish communities in London who are often overlooked. 72 1) Nominal record linkage of the MCR with the OBP 2) Keyword searching of Irish geographic terms and subsequent close reading 3) Surname analysis (onomastics) of defendants.…”

Section: Resultsmentioning

confidence: 99%

How Criminal Were the Irish? Bias in the Detection of London Currency Crime, 1797–1821

Crymble

2017

The London Journal

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Currency-related crime was endemic in London during the Restriction Period . This article looks at 884 individuals suspected or charged by the Bank of England, and considers how changes in detection strategy affected the prevalence of ethnically Irish people within that list of suspects. It rejects an anti-Irish bias, and concludes that from 1812 a reduced reliance upon shopkeepers to catch people passing off false currency, and a subsequent rise in 'sting operations' initiated by paid officers and local informants, resulted in a significant increase in non-Irish culprits coming under suspicion and a proportionate decline of Irish accused. This change was the result of the Bank's newfound ability to target local networks involved in the less public forms of currency crime (selling, counterfeiting, forging) for which the Irish were less well known. These findings challenge the Irish criminal reputation by highlighting the important role of detection strategies in accusations.keywords Crime, Ethnicity, Irish, Bank of England, Discretion, OnomasticsThe early nineteenth-century Irish in London are often remembered as poor, semi-criminal slum dwellers, associated with the narrow streets of St. Giles-in-the-Fields, the dockside parishes of the East End, and the Borough. Apart from internal English migrants, they were the city's largest group of outsiders. An 1803 estimate pegged the number of Irish beggars at 5,300, in addition to London's substantial financially secure Irish population. By focusing on the pre-trial stages of the justice system, this paper tests King's findings and the Irish criminal reputation, asking instead if Irish culprits were just more likely to get caught. This builds upon the substantial historiography of discretionary justice, which until recently has focused on class and gender rather than ethnicity. 5 Contemporary reliance on victim-led prosecutions makes pre-trial bias particularly important. 6 As John Beattie and Andrew Harris have noted, victims may have had assistance and advice from local officers or watchmen, but detecting the crime, identifying the culprit, confronting them, and seeking the arrest warrant were usually decisions for the victim alone.7 If anti-Irish bias was present when people made the choice to pursue or not pursue justice, then the Irish reputation for crime may be exaggerated.Anti-Irish bias was certainly on the minds of the Irish, who had a proverb 'the name of an Irishman is enough to hang him'.8 Suspicion of authority also resulted in occasional interventions by Irish men physically blocking attempts to arrest Irish culprits.9 Yet, pre-trial anti-Irish bias has proved difficult to measure, in part because those who got away form part of the elusive 'dark figure' of unreported crime.10 As J.S. Cockburn notes, the problem is compounded by the fact that the paper trail of justice was created for administrative and practical purposes rather than to accurately represent events for the benefit of historians. 11This paper addresses these challenges by focusing on an i...

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Section: Resultsmentioning

confidence: 99%

How Criminal Were the Irish? Bias in the Detection of London Currency Crime, 1797–1821

Crymble

2017

The London Journal

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“…For example, in [ 24 ] supervised learning techniques were employed for record linkage procedures in the study of social mobility between Europe and North America using censuses thirty years apart (1850-1880). Also, record linkage, geographic keywords, and surname analysis was used in [ 25 ] to identify the Irish in the long eighteenth century London in the 1841 census. Unsupervised and supervised learning techniques have been also used for analyzing contemporary census data as in [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

Managing the 1920s’ Chilean educational crisis: A historical view combined with machine learning

2018

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In the first decades of the 20th century, political actors diagnosed the incubation of a crisis in the Chilean schooling process. Low rates of enrollment, literacy, and attendance, inefficiency in the use of resources, poverty, and a reduced number of schools were the main factors explaining the crisis. As a response, the Law on Compulsory Primary Education, considering mandatory for children between 6 and 14 years old to attend any school for at least four years, was passed in 1920. Using data from Censuses of the Republic of Chile from 1920 and 1930, reports of the Ministry of Justice, the Ministry of Education, and the Statistical Yearbooks between 1895 and 1930, we apply machine learning techniques (clustering and decision trees) to assess the impact of this law on the Chilean schooling process between 1920 and 1930. We conclude that the law had a positive impact on the schooling indicators in this period. Even though it did not overcome the differences between urban and rural zones, it brought about a general improvement of the schooling process and a more efficient use of resources and infrastructure in both big urban centers and small-urban and rural zones, thereby managing the so-called crisis of the Republic.

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“…File with names and name ratios We start by using the names from the main Old Bailey dataset (both surnames and first names, undergoing similar cleaning steps as described for the Census names). We merge these names with the Historically Irish Surnames by Crymble (2015), retrieving a list of Old Bailey names with (when available) their rootname and name variants of the same surname. Next, we merge these Old Bailey names (both surnames and first names separately) with the Census names and name information (ratios): We start with matching by the original name, and then increase the matching rate by additionally matching by the respective rootname and name variants of the name in the Old Bailey records.…”

Section: Matching By Namementioning

confidence: 99%

Murphy's Law or Luck of the Irish? Disparate Treatment of the Irish in 19th Century Courts

et al. 2023

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A Comparative Approach to Identifying the Irish in Long Eighteenth-Century London

Cited by 5 publications

References 37 publications

How Criminal Were the Irish? Bias in the Detection of London Currency Crime, 1797–1821

How Criminal Were the Irish? Bias in the Detection of London Currency Crime, 1797–1821

Managing the 1920s’ Chilean educational crisis: A historical view combined with machine learning

Murphy's Law or Luck of the Irish? Disparate Treatment of the Irish in 19th Century Courts

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