Computational archival science – what is it?

Recently, I submitted a paper to the Computational Archival Science (CAS) workshop about my research exploring ways to develop tools and methods to support and undertake automated appraisal for cultural heritage. The project is at the conception stage and I am exploring the existing methods and tools that identify data from documents and map them as networked contexts. I am starting with a pilot project focusing on the Zoetic Walls street art project in Cleveland.

The project is heavily conceptual and explores what it means to document and manage cultural heritage that exists physically and virtually, and has significant ephemerality issues. The goal is to explore how it is possible to do this work in a way that engages with multiple stakeholders and various contexts that contribute to a social phenomenon that has been given some meaning and value. The ultimate goal is to design appraisal tools that might be able to be used in a much more participatory way. It is heavily conceptual right now and right at the beginning. A great place for some feedback from people who are interested in archival science and data. Or so I thought.

My short paper for the workshop was rejected, although the reviewers said it was well written. From the comments and then a closer reading of the workshop history and goals, I realise that I was trying to pitch a research project about archives to mostly digital humanities scholars who have their own particular view about what CAS is as well as what archival science is more broadly. I wrote an email responding to the reviewer comments, but it bounced back as it one of those email boxes that are not read. I could not find an email address for them either, so I have copied the email I wrote below.

The main issues that the reviewers seemed to have was that my work was too conceptual and that the conceptual aspects I was talking about are not “computational archival science.” Not just not suited or not part of the conversation, but actually not CAS. The email I wrote addresses comments from the second reviewer mostly and while I have not copied the second reviewer’s comments here, I think you get the gist from my email.

I feel like I must have missed a conversation somewhere about the relationship between computational archival science has with the actual discipline of archival science (or archival studies as it is also called here in the US). Are there any archival scholars out there who have been involved in this CAS that can enlighten me? I have read the definition listed on the website and wonder primarily about how the term, “archival thinking” has been used and its relationship use of “archival science.” I checked and I believe my work fit into the notion of Computational Thinking (CT) as being “the thought processes involved in formulating a problem and expressing its solution(s) in such a way that a computer—human or machine—can effectively carry out”. Of course I am happy to receive feedback and improve on my work and I can see where I could improve on the paper to expressly address CT, especially at this pilot stage. However, my concern remains this issue with what “archival thinking” means and how this is carried out in CT contexts as CAS.

To the workshop organizers;

Thank you for the feedback. From the reviewer feedback it is clearer to me that this workshop focus is on data science and machine learning from digital humanities perspectives.

It is unfortunate to hear that while engagement with archival science theories and principles is being asked for, engagement with archives and archival work is not suitable for the workshop. I am of course engaged with exploring what it means to document cultural heritage from an archives-as-institution point of view as this one of the ways that big data and linked data gets created. However, my research also attempts to grapple with alternative conceptual, practical and technological approaches to appraisal via crafting and testing methods to identify data as context (and then what that data tells us about the social phenomenon). I do see that I did not explore the notion of automation enough from an archival processing point of view and the potential role it can play for digital humanities research.

Regards literature on conceptual modelling, I am not addressing or using conceptual modelling, rather context entity mapping and network analysis using existing archival science standards and methodologies. It is possible to explore links between conceptual modelling from a computer science and information systems point of view, but it is really the topic for another paper. In archival science there is not much literature on conceptual modelling other than possibly the OAIS model (which is a functional model, albeit from a computer science idea of conceptual modelling) or the models developed from InterPARES (mostly business process models), or the records continuum model (conceptual and activity model) and other entity models developed by the Records Continuum Research Group (context entity models), so I am not sure what was expected in relation to this.

Regards the comment about being heavily influenced by critical theory, I do not refer to or engage in any critical theory literature or frameworks in this paper. As was mentioned clearly in the paper, my paradigmatic approach is social constructivism. The comment about being heavily influenced by critical theory in archives indicates a lack of understanding about engagement by archival science scholars with critical theory.  

Finally, it is regrettable that a workshop on archival science does not want to engage with the “four walls” of the archives and provide a space to engage with challenges that can be made from within and outside of this context. I do not work within those four walls, but I study them and what they mean in various conceptual, technological and practical ways, and any engagement with archival science, computational or not, requires exploration of their impact and meaning.  

Thank you for your time.

Regards,

Leisa