And that, in turn, got me thinking about how much freely available source material (primary and secondary) I’ve randomly stumbled across on local historical societies’ websites in the last few years. And wondering: how much more is out there?
I got several great responses to this*, so I began looking more closely, and the TLDR; answer is: a helluva lot of it. The upshot was a Google spreadsheet, which you can see at the bottom of this post.
I’m genuinely impressed at how much stuff these societies have put online, and several more are clearly keen to follow suit – if they can fund it. Some have adopted a pragmatic policy of embargoing their most recent publications (anything between 3 and 10 years; and if you really can’t wait that long, you can buy print or digital copies – or a subscription – for sod all). They often have limited resources, and good quality digitisation isn’t cheap. So, y’know, do encourage societies of interest to you to do this; but don’t lecture them if they haven’t (you might consider instead how you could actually help them to do it).
It’s also more generally noteworthy how many societies have websites (some kept more up to date than others…), and even if they haven’t digitised the publications, nearly all have made finding aids of some sort (indexes, TOCs, abstracts, etc, even searchable databases) available.
(And undoubtedly all this applies far beyond England and Wales, but someone else will have to compile those resources. Sorry.)
Why am I telling you all this? Because these local societies (under their many and varied names: “record/historical/antiquarian/archaeological” society, or some entirely quirky local name) are treasure troves for historians, not just those who think of themselves as “local” historians. They’ve been around for a long time (many were established in the 19th century), publishing high-quality source editions, calendars, abstracts, extracts, indexes, etc, for a wide range of archive sources – parish, legal and administrative, personal, estate records, and more – as well as secondary articles. But often they were published in tiny print runs and even finding aids were hard to come by before the advent of the online catalogue. So it’s a wondrous thing that so many can now be accessed freely and located much more easily.
In addition to content found at society websites, I added a couple more tabs to the spreadsheet: some of the many publications digitised for Welsh Journals Online, and an undoubtedly tiny portion of what might be found at the Internet Archive. Enjoy exploring!
An extended version of my paper for the April 2019 workshop held by the AHRC Research Network on Petitions and Petitioning from the Medieval Period to the Present, on the theme Petitioning in Context: when and why do petitions matter?
The paper uses data from the London Lives Petitions Project to explore the decline in female petitioning and rise in petitions from institutions in 18th-century London.
The Old Bailey Voices data is the result of work I’ve done for the Voices of Authority research theme for the Digital Panopticon project. This will be the first of a few blog posts in which I start to dig deeper into the data. First I’ll review the general trends in trials, verdicts and speech, and then I’ll look a bit more closely at defendants’ gender. …
This post takes a look at an open dataset available through the University of Pennsylvania’s open access repository. The dataset, Indentures and Apprentices made by Philadelphia Overseers of the Poor, 1751-1799 (created by Billy G. Smith), is one of an interesting collection of datasets on 18th- and 19th-century history which I may return to in the future. …
If you know me, the topic of this first post may come as unsurprising but also a bit eyebrow-raising. “Sharon, you’ve been working on the Old Bailey Online project (OBO) since forever. Aren’t you bored with it yet?” …
This dataset makes accessible the uniquely comprehensive records of vagrant removal from, through, and back to Middlesex, encompassing the details of some 14,789 removals (either forcibly or voluntarily) of people as vagrants between 1777 and 1786. It includes people ejected from London as vagrants, and those sent back to London from counties beyond
They’ve already written about this data in an excellent article (open access) and Crymble has blogged further about his ongoing research. (They have better visualisations too, so you could skip this post entirely and go to the real thing. Think of this as a taster.)
I want to focus on ways of visualising multiple categories of qualitative information – the more categories you want to compare at the same time, the more complex a dataviz has to be. In this case, I’ve got four categories to play with: gender, dates, countries of origin, and vagrant ‘types’. That’s to say, there are three types of individual in the dataset: leaders of family groups, their dependents, and single vagrants. The gender of the majority of dependents is unknown (most are children), so for most of this post, I decided to simplify things by filtering out all of the dependents to focus on the group leaders and singles. (As a result, because I’m ignoring about 500 wives who were counted as dependents, the following will differ somewhat from the work referenced above.) This resulted in 10963 individuals.
Overall, the gender ratio of the vagrants looks almost perfectly balanced (5438 female to 5525 male). But this hides some interesting variations.
Firstly let’s break it down by the year of the case. (There are some missing records, and the very small numbers in 1777 and 1779 in particular are due to these gaps.) Two things stand out: the numbers of both female and male vagrants rise rapidly in the mid-1780s; and women are in the majority each year until 1782, after which they’re overtaken by men.
Now looking at vagrant type. As soon as you have multiple categories, you can split up the data in different ways – the “best” can depend on the data and exactly what it is you want to show. So graph 3a compares the percentages of male and female vagrants for each vagrant type, whereas graph 3b shows the percentages of group and single for each gender. 3b highlights that the majority were single individuals – something you wouldn’t know at all from 3a. It also makes it clear that vagrant type was gendered – considerably more men than women were singles. 3a, on the other hand, is better if you want to know exactly what the proportions of men and women were in each type. Most often, if I had to pick just one of these, it’s likely that I’d plump for 3b, because I’ve already seen that overall there are very similar numbers of men and women. But it might be a harder choice if that weren’t the case.
Now, looking at country of origin (British and Irish vagrants only, as there were only a few from other countries ), further striking differences emerge. It’s hardly surprising that the majority of the vagrants came from England, but much more noteworthy that there was such a large disparity between Irish men and women.
Adam Crymble discusses what’s most likely going on, and it ties in with the particularly rapid increase in the numbers of male vagrants from 1783 shown in graph 1 – it’s probably the result of demobilisation after the American wars.
This says ‘demobilisation’ to me, and the male nature of most Irish vagrants suggests that this may have been a strategy for getting home after the war. Demobilisation was heavily centralized in London. Soldiers and sailors weren’t taken home; they were dropped off and left to find their own way.
Finally, I want to visualise the relationships between three categories in the data: gender, country and vagrant type. Mosaic plots are a more complex and less commonly used type of visualisation that can cram a lot more information into a single chart than you can with a bar chart. But, as with boxplots, that makes them a bit harder to interpret.
Imagine that you start with a single large rectangular block. For your first category, you divide it horizontally, and put the labels for each “level” (in this case there are two, F and M, for gender) on the left hand Y axis. As in the very first bar chart, we can see that the proportions of men and women are close to equal.
Then you sub-divide the two blocks vertically for your second category (country) and put the labels along the top X axis. So reading left to right along each gender block, the first vertical block = English, the second = Irish, third = Scottish and fourth = Welsh. Again, we can see that English vagrants are in the majority for both genders, and at the same time, how a much higher proportion of the men are Irish.
Finally you sub-divide the blocks once again, horizontally, for the third category (vagrant type), and the labels for these (group and single) go on the right hand Y axis. The biggest single category, then, is women from England who are single (Hitchcock et al argue the importance of short-distance female migration London to find domestic service for making up much of this). The smallest category is men from Wales who lead a group.
Male Irish and Welsh vagrants are more likely to be single than are men from England and Scotland, whereas a higher proportion of Irish and (even more so) Scottish women were heading groups. (Crymble has also emphasised how different the Irish and Scottish vagrants were.)
The use of colour and shading adds one final dimension, but it’s harder to interpret on first sight. The idea is to show statistical significance. What it boils down to is that blue means the square is bigger than would be expected by the statistical model; red means it’s smaller than the model would expect (and the darker the colour, the bigger the significance). The fact that the group-Irish-male box is coloured dark red (ie, smaller than “expected”) pretty much seems to reinforce what we’ve already observed. The group-Scottish-female box also stands out among the smaller blocks – suggesting that this is significant and might be further investigated.
However, it’s important to to understand whether what the statistical model “expects” is appropriate for the data we have. In medical research, where data collection is conducted according to carefully defined rules, it may be possible to be confident that a statistical significance means a “real” difference. For a historian it might simply be pointing to imperfections in the data! So it’s essential for historians doing data analysis and visualisation to get to grips with both the original sources and the statistics. I’m still grappling with the second part…
The petition of Geelien Cowley ‘a poore widdow and mother of three smale fatherlesse children’:
that your petitioners late husband by name E[dward] Birien of Ruthin a souldier that served in his majestys service in Ireland neare upon three yeares & afterward he retorned to England he served in his majestys service there sixe or seaven yeares where in all these tymes he suffered many ympriso[nments] wounds & brueses wch made him unable to earn his liveliehoode & more especiallie this two yeares last past then he was allowed one of the majestys pensioners to receave a share of his majestys allo[wance] for maymed souldiers provided. Nowe may it please [your] worships to be advertised that the said Edward Birien your petitioners late husband, had a longe sicknesse, beeinge vearie poore & nowe called to gods mercie caused your petitioner to goe upon the credit with her neighbours to suplie her said husbands wants in confidence to receave his share & alloweance of pension as afore is set forth, but it was gods will to take hime to his mercie afore this generall sessions.
Most humbly prayeinge your worships to allowe your petitioner the pencion allotted her late husband for to paye to her creditors what she is engaged for & your worships further help & succours in such sort as your worships thinke meete without your worships comisseracion hearein your petitioner shall not be able to goe amonge good & charitable people for releefe to her & her smale children for feare of arrest or lawsuite. this I humblie bege for gods sacke…
The treasurer of the maimed soldiers’ fund was ordered to pay her the whole quarterly allowance due to her husband.
[NLW Chirk Castle Quarter Sessions files October 1665 B21/d7]