Hi, my name is

Stijn Conix

I help businesses, policy-makers and academics turn complex data into robust decisions and replicable science.

My work is grounded in peer-reviewed research. I design structured models that make assumptions explicit and results interpretable.

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About

I have nearly a decade of experience as an academic researcher, working across the University of Cambridge, KU Leuven, and UC Louvain. My research has spanned multiple fields, including digital humanities, philosophy of science, and biodiversity science, and has resulted in 46 peer-reviewed publications and over 500 citations.

Alongside academic research, I have been actively involved in policy and governance contexts. This included advising multilateral environmental agreements and international organisations such as the Catalogue of Life, and contributing to expert committees where research outcomes needed to be translated into concrete decisions, standards, and frameworks.

More recently, I have applied this background outside academia by helping the largest Belgian shoe retailer design and structure its charity and social-impact strategy. In this role, I drew on insights from development economics, ethics, and health economics to help the company allocate its charity budget in a way that best realized their impact goals. At the moment I am working with them to implement a marketing analytics framework

What I Can Help With

  • Designing and analysing experiments
  • Support for responsible research practices (data management plans, preregistration, open data)
  • Clear visualisation and communication of statistical results
  • Support with writing, reviewing, and strengthening methodology and results sections
  • Translating established academic evidence into usable insights and decisions, adapted to real-world data, constraints, and timelines
    For example: adapting state-of-the-art statistical models from marketing research so they work at scale with noisy data and the operational constraints of practical decision-making.
  • Quantifying what marketing contributes to sales and growth using aggregated, post-cookie data, to improve the return on marketing spend
    For example: identifying which channels or campaigns can be scaled profitably, and which budgets can be reduced or reallocated without hurting sales.
  • Designing and analysing experiments to identify which actions or interventions actually work
    For example: testing whether a pricing change, promotion, website adjustment, or customer communication leads to a meaningful improvement, rather than relying on intuition or past correlations.
  • Supporting decisions about whether and how to act under uncertainty, using explicit estimates of risks and chances
    For example: comparing the expected upside and downside of launching a new campaign versus doing nothing, including the probability that the effect is negligible or negative.
  • Using existing observational data to answer concrete policy questions and to compare plausible funding or governance interventions before committing to them
    For example: analysing past evaluation procedures to assess which design choices are associated with stronger scientific outputs or fewer unintended effects.
  • Designing and setting up experiments or pilot programmes to test policy interventions before scaling them up
    For example: trialling alternative grant evaluation criteria, review processes, or incentive structures on a subset of calls to measure their effects.
  • Measuring the effectiveness of new policies during rollout, rather than only evaluating them retrospectively
    For example: real-time tracking whether a new funding instrument changes researcher behaviour, collaboration patterns, or research outputs as intended.
  • Grounding policy design and reform in the scientific literature, while translating that evidence to real-world institutional constraints
    For example: connecting insights from metascience, economics of science, and evaluation research to the practical realities of funding agencies and ministries.
I apply the same standards in academic, applied and policy work: explicit assumptions, transparent reasoning, and careful treatment of uncertainty, with a focus on analyses that are robust, interpretable, and decision-relevant.

Selected Work & Publications

Peer Review & the Relevance of Humanities Research
Bayesian modeling Science Policy Meta-Science
Peer Review & the Relevance of Humanities Research
Study that employs a Bayesian thurstonian model to model how raters judge the relevance of humanities research. We estimate the chauvinism and strictness of the raters, as well as the underlying relevance of the research papers they rate.
Survey on the ethics of research funding
Bayesian modeling Research integrity Meta-Science Science Policy
Survey on the ethics of research funding
Self-report survey to investigate the prevalence of questionnable research practices in the context of research funding.
Measuring disagreement about bird taxonomy
Bayesian modeling Biodiversity Science Meta-Science Digital Humanities
Measuring disagreement about bird taxonomy
Study that develops a measure for disagreement about different bird taxonomies, and uses this measure to understand what drives disagreement and what we might do about it.
Taxonomic disagreement about ranks in gray-area taxa
Bayesian modeling Biodiversity Science Meta-Science Digital Humanities
Taxonomic disagreement about ranks in gray-area taxa
A vignette study that delves into the patterns and causes of taxonomic disagreement about species-level ranking decisions.
The costs of competition in distributing scarce research funds
Meta-Science Science Policy
The costs of competition in distributing scarce research funds
An opinion paper urging policy-makers and funding agencies to do more research on the best way to distribute research funding.
Measuring the isolation of research topics in philosophy
Science Policy Meta-Science Digital Humanities
Measuring the isolation of research topics in philosophy
Bibliometric study that tries to quantify and analyse the academic connectedness and isolation of research topics in various areas of academic philosophy.

Academic Expertise

Meta-Science & Research Policy -

Studying the scientific process itself, including peer review, funding mechanisms, and research integrity.

Selected Publications:

Biodiversity Science and Taxonomy -

Quantitative and qualitative approaches to understanding taxonomic disagreement and its implications for biodiversity research.

Selected Publications:

Science, Society and Values -

Philosophical research into the intersection between science and society.

Selected Publications:

Education and Experience

2023 - 2025
PostDoc Digital Humanities
Postdoctoral researcher at the Pence Lab at UCLouvain, developing and analysing a corpus of taxonomic literature through a variety of methods such as topic modeling, natural language processing, and bayesian modeling.
2021 - 2022
Interdisciplinary Postdoc Meta-Science
Project manager and main researcher on a project on taxonomic disorder at the Centre for Logic and Philosophy of Science at KU Leuven.
2018 - 2020
Postdoc Bias in Philosophy
Postdoctoral researcher working on bias in academic philosophy at KU Leuven.
2014 - 2018
PhD in History and Philosophy of Science
PhD on the role of values in taxonomy at the HPS department, University of Cambridge.