Academic age

In principle, academic age is simple: the number of years since you completed the PhD. The idea is that your physical age is irrelevant when we evaluate your track records (publications, citations). Instead, we consider how much time you had to build that track.

So far, I guess we’re all on board. However, just looking at the time since your PhD (is that the viva or the graduation ceremony, by the way?) is inadequate if you could not actually do research, like if you were working in industry or engaged in clinical activities (i.e. non-academic employment) or were on parental leave. We can account for this, like the new SNSF CV do, in the spirit of the DORA declaration:

The net academic age is the amount of time you have actually been able to dedicate to research, after deducting interruptions and non-scientific work.

The SNSF template offers: maternity; paternity, adoption, parental leave [as a separate category]; illness or accident; care duties; public service; continuing education; non-academic employment or clinical activities; part-time work; unemployment; other.

However, the more I think about it, the more complicated it gets. For instance, it is increasingly common for PhD students to do paper dissertations, that is, they write a series of academic journal articles rather than a monograph. In terms of research undertaken, this is equivalent, but in terms of publications and citations, a paper dissertation gives you a head start over a classic monograph that may be published a year or so after graduation.

Parental leave and reduced percentages for care reasons seem pretty straight-forward. But what about those who keep working on their research projects during parental leave, do they get a deduction because technically they were on leave, or not because effectively they worked?

Where does academic employment end (or is scientific work the relevant concept? — the guidelines are vague)? In my case, I do a substantial part of my work consulting on commissioned work for government offices etc. Is this academic employment because it entails research and (basic) scientific methods, is this academic employment because the job is at a university (but what if I did the same work in industry, like many of our competitors?), or is this non-academic employment because we produce grey literature (reports that may or may not be published, no peer-review), or because we do not choose our own research question, or because these publications do not count (or even count negatively!) when it comes to tenure prospects?

How do we account for stability? A string of short-term jobs where you spend much of your time applying for the next opportunity are a different beast than tenure-track or tenured positions! And indeed, even two seemingly stable, tenured jobs can be quite different in terms of teaching load. If your net academic age does not consider the fact that some academics can focus all their energy on research, while others are teaching staff doing as much research on the sides as they can, the aim of “allowing us to make a fair comparison with other applicants” is a hollow promise.

I don’t know, we can also add administration and management duties, a boss who gives you the freedom to undertake whatever research you want (or no boss at all) versus a boss who tells you exactly what to work on (e.g. in a project), the resources you have available like research assistants or money to field work or experiments, if authorship practices in your team differ from common practice in your discipline…

And then we get evaluated by criteria like “excellence” (for grants) or “fit” (for jobs) that are as vague as they are explicit (as DORA requires) — does it really make things better if we adjust for net academic age?

This is how we do research ethics at the SFM (update)

A while ago, I shared how we do research ethics at the Swiss Forum for Migration and Population Studies (SFM). Given that we often do commissioned research at the SFM, it’s important that the administrative burden is kept low, but we do and want to take ethical questions serious. Here I share the updated guidelines that I have put together for the institute, recently further streamlined. The aim remains to encourage all researchers to think about and take research ethics serious, and the guidelines are a synthesis of other ethics guidelines (duly acknowledged).

The guidelines begin with a short flowchart to deal with the most common cases. The list of exempt cases is now more explicit, with the understanding that if researchers identify ethical questions in seemingly benign approaches such as a literature review (e.g. because of the way the research question is posed, or because of the funder) can require a more thorough reflection (and thus the checklist to be filled in).

The core of the guidelines remains a checklist with 11 question. Each question — like “Does the research involve sensitive topics?” — comes with a few examples, and there are three possible responses: yes, uncertain, no. Researchers can tick the appropriate boxes, but it proved useful to use numbers for “yes” and “uncertain” answers to facilitate cross-referencing with part 2 of the guidelines.

Where some of the answers are “yes” or “uncertain”, researchers fill in part 2. Now more detail is required, including a brief description of the work, but normally the longest part is “Steps taken to address ethical issues”. Here the cross-references come in handy. If I identified 3 issues in part 1, I can now refer to them by number.

An uncontroversial list of ethical principles like “no harm to subjects and researcher” or “informed consent should normally be obtained” is included at the end of the checklist.

With the streamlined design, for some projects the ethical checklist takes only a short moment (e.g. literature review, analysis of secondary data where individuals are not identifiable). For other projects, we can typically handle the situation at the institutional level (e.g. interviews), while occasionally we want to have a thorough examination by the ethics commission of the university (e.g. field experiments).