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We have already learned why diversity is important for performance and innovation. Here we explore how a focus on potential, rather than status, and an acknowledgement of privilege and bias, can support a meritocracy and lead to inclusive practices.

Advocates of meritocratic systems stress that everyone has an equal chance to advance and obtain rewards based on their individual merits and efforts, regardless of their gender, race, class, or other non-merit factors.  However, research suggests that an emphasis on meritocratic practices can have the unintended consequence of increasing bias1, therefore leading to inequity.   Meritocracy can also fail to take account of unearned privilege.  Those that belong in the more socially desirable demographic groups (based on, for example, race, class, education, gender) benefit from a conferred status that is not related to performance and this advantage can continue and be gradually enhanced throughout their career.

I think the thing about the merit picture is that you can’t have a discussion about things being merit-based if you don’t provide a level playing field. And the non-levelness of the playing field may not always be evident to the people making decisions...
- Prof Rachel Oliver, University of Cambridge and leader of The Inclusion Group for Equity in Research in STEMM.2

In the same way, a merit-based approach can fail to recognise the challenges faced, and overcome, by people navigating inherently racist, ableist, sexist and neurotypical systems.

To ensure that merit-based recruitment, promotion, and reward are equitable, we must become more aware of the role of unearned privilege and bias in our decision-making processes.  It’s natural to like people who have similarities to us but homogeneity has an adverse impact on diversity.  We need to find ways to recruit and retain talented individuals based on their potential, rather than their status.    This approach supports genuine meritocracy and builds inclusion into our processes and practices.


Essential CriteriaWhat are you really looking for in a person?Identify & specify the  specific behaviours, skills and knowledge that are actually required for the role and/or award.Rate their importance.These form your measurable essential criteria.  By getting this right at the start you can mitigate unconscious bias.Attract  a Broader Range of TalentAdvertise more broadly - use social media and link with subject specific networks. Encourage applicants from different educational, cultural, and socio-economic backgrounds.Reach out to groups that will bring balance to your applicant pool.Don't rely solely on metrics to assess potential - they are not a good predictor of future work performance or invisible skills. (Note:  A number of funders have introduced Narrative CVs  to address this).Remove Bias from ProcessesBe aware of your own biases - take Implicit Bias training before undertaking decision-making processes (including recruitment & promotion).Avoid homogeneity.Look for potential in candidates, rather than relying on perceived skills.Ask structured interview or assessment questions that relate directly to the essential criteria.Assess people against how they perform, not how they look, where they were educated, who they are, or where they are from.Monitor & ReviewUse data to identify whether a diverse range of people are being recruited, and are progressing, within your group or team.If not, ask yourself why - is the process at fault? Are the criteria objective and inclusive? Is bias to blame?Review and revise your processes to reduce the potential for bias and to improve diversity and inclusion.


For the purposes of data collection, statistics, and reporting, the collective term ‘BAME’ (black and minority ethnic) is often used, and where we refer to such data, we use the same terminology.  However, we are aware of the difficulties and limitations around using such broad terminology.  ‘BAME’ is frequently used to group all ethnic minorities together which can disguise disparities in outcomes and experiences between different groups. It also emphasises certain groups (Asian and Black) and excludes others that also face negative disparities.  Most importantly, many people from minoritised groups themselves say they do not like the term.

Where possible, we aim to use specific ethnic classifications and related data to ensure that we are not overlooking issues faced by particular groups. 

References & Resources

1      Castilla, E. J., & Benard, S. (2010). The Paradox of Meritocracy in Organizations. Administrative Science Quarterly, 55(4), 543–576. 

2        Prasad, A (2021).  Why are there still so few black scientists in the UK?  The Guardian, 10 April 2021Why are there still so few black scientists in the UK? | Science | The Guardian

Poster Data Sources

Other Resources


Image of a poster discussing a merit based approach to recruitment and how this is impacted by disparities in experience.  Text reads as follows:Academic institutions aim to admit students and recruit staff on the basis of meritBut merit can only be assessed objectively in the overarching context of external factors, such as disparities in education, healthcare, personal finances, housing and the judicial system.  6 boxes describe some of the disparities that exist:1. Fewer Black school leavers (6%)attend a Russell Groupuniversity than White school leavers (11%).  2. A disproportionately small number of Black, Black-Mixed and Asian postgraduates are awarded UKRI-funded PhD studentships. 3. BAME applicants for academic positions in Oxford are half as likely to be successfulas White applicants. 4. Success rates for Wellcome Trust grants are lower for Black and BAME applicants (8.6% & 10.3%)tHan White applicants (16.1%). 5. BAME applicants have to send 60% more applications to get a positive response from an employer than White candidates. 6. Black workers with degreesearn 23.1% less on average than White workers (UK).There are links to previous posters to learn more,  find out what can be done and learn how diversity makes science better: