  Various processes in academic organizations include the decision points where
selecting people through their assessment and ranking is performed, and the
impact of wrong or right choices can be very high. How do we simultaneously
ensure that these selection decisions are well balanced, fair, and unbiased by
satisfying the key stakeholders' wishes? How much and what kinds of evidence
must be used to make them? How can both the evidence and the procedures be made
transparent and unambitious for everyone? In this paper, we suggest a set of
so-called deep evidence-based analytics, which is applied on top of the
collective awareness platform (portal for managing higher education processes).
The deep evidence, in addition to the facts about the individual academic
achievements of personnel, includes the evidence about individual rewards.
However, what is more important is that such evidence also includes explicit
individual value systems (formalized personal preferences in the
self-assessment of both achievements and the rewards). We provide formalized
procedures that can be used to drive the academic assessment and selection
processes within universities based on advanced (deep) evidence and with
different balances of decision power between administrations and personnel. We
also show how this analytics enables computational evidence for some abstract
properties of an academic organization related to its organizational culture,
such as organizational democracy, justice, and work passion. We present the
analytics together with examples of its actual use within Ukrainian higher
education at the Trust portal.
