The article in a nut shell seeks to push ethnographers towards thinking about ethical implications of their research and how to develop standards of transparency that are consistent with their relationship with their subjects and scholarship. In this regards the authors focuses on fourn stages of the research writing process, namely:
1. Recording and collecting data: The accuracy of any data differs based on the methods that is used to collect it. Therefore, the article pushes for transparency in how the data is collected and the methods that is used to do so, and in particular, bringing in the study of digital spaces in ethnographic research.
2. Anonymizing: Furthemore, transparency during the research and writing process should also be maintained in how researchers employ methods of anonymization in their research and in their relationship to their subjects as well as the rationale behind such decisions.
3. Data verification: Laying bare the grounds on which the research draws it conclusions or insights, the article urges ethnographers to indulge in transparency when they write their papers / books so that the readers can see and judge the validity of the source of their data
4. Destroying, preserving and sharing data: The article pushes for an active and ethical forms of collaborations between researchers in terms of sharing their data.
This text addresses transparency in ethnography and the use of new technology. It suggests standards for transparency and discusses ethical implications. It contributes to theory and practice of archive ethnography by building standards for ethnography practice. The paper specifically attempts to answer how ethnographers have answered to the second reckoning in ethnographic research: confidentiality and data verification. They argue that standards for recording data, collecting data, anonymization, verification, and data sharing would bolster transparency and replicability.
In the section about fact checking, the authors state:
"At the very least, then, providing a paper trail of one’s verification efforts in parentheticals or endnotes will allow readers to assess whether or not the author has convincingly made the case with the data at hand" (my highlight).
The concrete example of how to include different ways of fact checking is helpful when thinking about traditional ethnographies in the form of books. I think we could add the arguement that the length, scope and readers engagement with this "paper trail" could be creatively built into archives (if not requires a form of archive to keep up with it).
Thinking through different stakeholders of an archive is one way to approach the design question: which paper trail for which audience, with which standards and limits? The article deliberately focuses on ethnographers, but also points to journalists, who have different fact checking expectations. Further, we can ask what a peer-review of the paper trail (or archive) that each author is expected to create will look like -- including but also going beyond scholarly review articles like this one.
What concepts, ideas and examples from this text contribute to the theory and practice of archive ethnography?
Overall, Murphy et al. argue in favor of increasing the transparency of ethnographic research and data, though they recognize that ethnographers may differ in their positionality, the populations they study, and their institutional resources, and, thus, the degree to which ethnographers share their data and make their practices transparent, will be made on a case-by-case basis. For this piece, I think the concept of "reanalysis" rather than replication is essential when considering how tranparency and open access can be applied to archive ethnography. Though as the author's make note of, the level of transparency will depend on a case-by-case basis, scholars can use digital media and open access sharing platforms to their advantage as Adema's (2021) piece demonstrates. Ethnographic archives can be used to share and present data for reanalysis in various formats and, with various privacy settings such platforms offer, in ways that can still prioritize the safety and confidentiality of interlocutors. As Murphy et al. encourage, the least scholars can do moving forward is consider the ways they can make their own research practices more transparent and their data more accessible for sharing and reanalysis.
What evidence or examples support the main argument, narrative or e/affect?
Murphy et al. use evidence from the practices and arguments made by current ethnographers and other scholars/experts who have critiqued the data sharing practices of ethnographic research.
Exemplary quotes or images?
In particular I liked this quote which supports the argument the authors made as it pushes back against the notion that transparency is only important in terms of replication; whereas, the authors demonstrate that for ethnographic work, transparency should be promoted in terms of analysis of the work, not its replication:
“The fact that ethnography cannot be replicated or reproduced in the same way that quantitative research can does not mean that it should be immune to calls for greater transparency—quite the opposite. Transparency is crucial for meeting what we think should be the standard by which to judge whether an ethnography contributes to theory building and the accumulations of empirical knowledge about the social world: reanalysis” (p. 43)
(1) Data habits/practices as reflexivity and an ethnographic good: The move to locate this text's call for ethnographers to pay attention to their data practices (storing, preserving/destroying, sharing, analysis) as an extension or a recall to the "first reckoning" that called for ethnographers to pay attention to their emplacement, offers an important reason for transparency beyond calls by funders to open data. While it is more common now to see declarations of positionality in ethnographic research (which is very important), t is not common practice for ethnographers to think about their data apart from IRB/funding confidentiality requirements, especially how interlocutors would engage with ethnographic data, informational ecosystems. The authors offer many tactics for ethnographers throughout the text to interrogate their data habits, and it would be quite useful to have a running list/doc for noting down all of those and associated citations.
I found their concluding remarks helpful to clarify their intentions. Italicizations are mine
“at a minimum, ethnographers can be reflexive and transparent around the decisions we make with regard to how we record our data, what quotations mean, whether we follow our participants online, and whether and how we anonymize. We can also, at a minimum, make in-text distinctions between data that come from interviews and data from observations and be transparent around what evidence we use to verify our claims. Other measures, especially making data publicly available, will require collective action and significant institutional support.”
(2) Learning of & from non-anthropological data practices: Throughout the text, I read references to confidentiality, fact-checking, and archiving practices of journalists, lawyers, archivists, quantitative social sciences, and them querying anthropologists in turn about their data practices. These conversations in one place are helpful to clarify differences and alliances between ethnography and journalism, for example, a question that I've often received from people when I tell them what I am doing: "Oh, you conduct interviews. How is that different from journalism?"; or between quantitative and qualitative data practices: "So what's your sample size?". Instead of feeling attacked by these questions or feeling like I have to justify ethnographic difference, this text offers me useful points of comparison.
(3) The concept of "reanalysis": Like other colleagues in this seminar have pointed out, the text's primary argument and intervention for data transparency is reanalysis, which can take many forms: revisiting old sites, revisiting fieldnotes, comparing or swapping fieldnotes; practices that happen clandestinely or in conference meetings anyway. Rather than pushing for open ethnographic data, the authors are pushing for openness as an ethnographic standard. It certainly unsettles.
Recording and collecting data: The authors urge ethnographers to be transparent about how they collect data, given the difference in accuracy between different methods (e.g. memory versus recorded interviews). They offer examples of ethnographers only using specific demarcations for speech that was audio recorded, and no demarcations for speech recounted from field notes. They also emphasize the increasing importance of taking digital spaces into account in ethnographic research.
Anonymizing: The authors encourage transparency in their work about the anonymization techniques they employed and why—both in their writing, and in communication with those participating in the study. They suggest this transparency should occur throughout the research and writing process.
Data verification: The authors encourage ethnographers to make transparent “how they know what they know” (52), in order to enable the scholarly community to evaluate the grounds on which an ethnographer is drawing conclusions.
Destroying, preserving and sharing data: Lastly, the authors argue that there is much to be gained by sharing data. They encourage ethnographers to collaborate in developing guidelines for data sharing that are consistent with ethical principles and take into account issues of positionality.
“We are suggesting that the time has come for ethnographers to work together to develop their own set of guidelines for data sharing that are consistent with research ethics principles and that account for issues revolving around positionality” (Murphy et al, 2021:55)
shifting meta-data to the text
Some of the authors suggestions seem to be pushing ethnographers to pull meta-data and other ingrained analytical tools into the audience's purview. For example, the authors promote using different quotation marks (single or double) to differentiate within text the source (e.g. a recorded quote versus a recalled quote) of an example. For the reader, this symbolic inclusion brings in a qualitative weight and descriptive scientific scene-setting that can alter a text's reception - but it also encourages researchers to think more closely about some of the meta-data they must collect and share.
Reanalysis, collaboration, data transparency and sharing, and online communities