It’s necessary to frequently remind ourselves that Academia does not just happen in English. It sounds like a silly thing to write, but having worked in the UK for a while I know it is rare to attend events that are not in English and it is common to ignore scientific communities and publications in other languages. This attitude is certainly encouraged by the Institute of Scientific Information (creators of our beloved Impact Factor) who rarely incorporate non-English language publications in their index. This is an assumption supported by some generalizing statistics: the majority of scientific publications are in English, the vast majority of citations are to publications written in English.
There is nothing wrong with one language emerging as the dominant one to facilitate academic communication. But this trend is inevitably accompanied by other language communities producing, debating, and evaluating work in English and their own language. This is necessary and facilitates non-English speakers to evaluate and contribute to international debates. Such communities enable those who are engaged in both international and national debates to cross-fertilize academic communities. Most importantly however, these will be the communities that take care of one of our most crucial duties as academics: to communicate our findings in a critical and understandable way to the general public, regardless of their language.
All of this is of course beside the point 🙂 I want to encourage everyone to attend the third French-speaking historical network science community conference. It’s a great and active community, with some genuinely nice and interesting people. This will not be a disappointment. I have engaged with this community before and came out with fresh ideas and approaches I could not have possibly gained within my English-speaking cocoon.
The third conference of the French-speaking group Res-Hist (réseaux et histoire – historical network analysis) will take place in Paris on the 29-31 October. The format mostly offers discussions of work in progress by historians, as well as presentations by specialists of other disciplines (geography, geomatics, sociology, law, anthropology, computer science) who have dealt with social networks in time, or social networks reconstructed from written sources. All those among you who understand French are welcome! Extended abstracts are put online when we receive them: feel free to comment on our website http://reshist.hypotheses.org, that also gives details on the conference program.
Let’s be honest: networks aren’t really the right answer for many research problems. But how can we evaluate this and do network science properly. This is a discussion post about this topics with a preliminary list of best practice guidelines. I will give a presentation on this topic at the EAA in Glasgow on Saturday at 8AM session AR8 but would like to stimulate discussion online as well, so engage!!!!! Get in touch, blog, tweet, comment.
It is uninformative for archaeologists and historians to use formal network methods as a hammer to hit every nail we can find with, just because we can. Specific formal network methods should preferably be selected in light of their ability to lead to insights that other approaches cannot offer. But what determines this usefulness of particular formal network methods for those studying the past? Is it the convenient representation of entities such as humans, islands, ports and the past interactions between them as dots and lines? Or is it the good fit between the past phenomena of trade, transportation and communication, with their abstraction as network concepts? Although these reasons might be sufficient to lead scholars to consider using formal network methods for addressing their research aims, they are not sufficient to motivate the adoption of specific network techniques.
I strongly believe that network science has something new to add to our disciplines, but a lot of work still needs to be done to leverage this promise and make it productive. To help us in this I think four things are needed:
Communities and events that provide a discussion platform for exploring this potential. The Connected Past and the Historical Networks Research communities have been providing venues for this task, and hopefully many more will follow.
Good practical examples should be published to give scholars an idea of how network science techniques could be beneficial in their own work, and to stimulate them to think creatively about applying these formal methods. The Connected Past publications alongside many others aim to serve this purpose.
These early examples should not just be accepted at face value but should be critiqued. To enable this, training should be provided to archaeologists and historians. Annual workshops have been provided by The Connected Past and the Historical Networks Research communities and at the CAA conference.
Finally, a community of archaeologists and historians should develop guidelines to best scientific practice in using these techniques. This could follow the format of the ADS guides to good practice.
I would like to call upon everyone interested in the use of network science for the study of the past to contribute to the development of these guidelines. Get in touch, blog, tweet, comment. Here is my attempt to develop a few very broad guidelines to good practice:
Network science techniques are methodological tools with clear rules and limitations.
Archaeologists could be provided with guides to good practice and archaeological examples, making them able to understand what kinds of questions different network science techniques are designed to answer and to evaluate whether it allows them to achieve their research aims. To do this hardly any familiarity with mathematical and computational techniques is required, only a willingness to explore the potential of a scientific method.
An evaluation of the potential contribution of network methods to addressing a particular research problem might be enhanced by working explicitly through the network science research process (Brandes et al. 2013), which again does not require much technical skills.
However, once archaeologists have decided to apply a specific network science technique, then a thorough understanding of the technical underpinnings of this technique is not an option but a prerequisite for a critical interpretation of its results. Archaeologists could be aided in this process by multi-disciplinary engagement and collaboration where possible.
Network concepts developed in network science are associated with specified data requirements, which should be acknowledged by the archaeologists adopting them. If the data requirements cannot be identified in empirical or simulated data then the network concepts loose all explanatory value.
When developing new network concepts, one should formulate network data specifications such that it becomes clear how the concept differs from exisiting concepts.
Formulating specifications of how network concepts are represented in network data allows for different conceptualisations of the same past phenomenon to be compared and possibly falsified.
A shift in perspective from the study of static structures to the emergence of empirical observations and past phenomena might be needed.
Confirmatory network science techniques offer archaeologists an approach to understanding how large-scale patterns emerge through the particular interactions of individual agents or relationships.
Confirmatory network science techniques can only be usefully applied when specifications are formulated of how the network concepts used should be represented as network data.
Confirmatory network science techniques require one to explicitly acknowledge the dynamic nature of past processes and the dynamic assumptions underlying the definition of ties. Because of this, I believe these techniques reveal the potential contribution of network science for archaeology far more than the exploratory network techniques.
The past systems we study were governed by dynamic phenomena and the network approach used to understand these phenomena should reflect their changeable nature.
Only in cases with a small number of nodes and where dependence assumptions gave rise to specific easily visually identifiable patterns, were network visualisations preferable over other types of data representation for communication purposes.
Even in cases where network science techniques do not offer additional functionality compared to other more common archaeological techniques, it could still lead to interesting insights by forcing one to explore a dataset or hypothesis through the lens of one’s assumptions about why and which relationships matter.
If a method is needed where the boundaries of entities are ill-defined and fluid, and where one argues these can not under any circumstances be tied down for analytical purposes, then network science does not offer the solution.
Network science can never be separated from the archaeological theoretical motivations of how and why certain archaeological evidence allows one to better understand a past phenomenon.
Some past processes are unknowable, due to our current techniques and datasets. All archaeological approaches suffer from this disadvantage and network science is no exception.