We publish openly available diversity reports spanning all our activities, and have done so since 2014. We are serious about learning both from our successes and our failures, and believe that transparency is key for sector-wide improvement.
The most recent 2019-20 diversity report is the Academy's sixth annual report of diversity data, and the fourth published externally. It spans the entire remit of our work, from grants to staffing, from Fellowship to events. For previous reports, data has been collated, analysed and reported on by Academy staff but in 2019-20 the data has been analysed and the report written by external consultants from Select Statistics and Inclusive Recruiting. See a summary below, or download the full report and explore previous year’s reports on the side of this page.
Based on their findings the authors make 8 key recommendations to be taken forward to progress the equality, diversity and inclusion (EDI) journey and impact for the Academy.
- The Academy should build an overarching EDI strategy with recommendations of the areas of priority built into long-term and short-term plans.
- The Academy needs to lead by example. An investment should be made for either an existing internal team member (as part of an existing role) or a new role to be created to own and steer the EDI change.
- A targeted approach to increase awareness and understanding of EDI and encourage learning and unlearning must take place across all Fellows, committees, and internal staff team members.
- Change must start within the Fellowship: this key area is the pipeline and delivers most expertise and decisions across many areas of the Academy. Getting inclusion right with the support of the Fellows will fundamentally and significantly change the entire EDI landscape for the Academy.
- There is a significant disparity within the Academy for representation from Black, Asian and minority ethnic groups (BAME) across all areas including governance, any event attendance, grants and internal staffing. Every area is underrepresented and requires urgent action to investigate and action change.
- An overarching review of assessments should be undertaken. There are many opportunities for grants, competition, employment and Fellowship achievement but there is no system or process to ensure inclusion happens during the scoring and assessing of people.
- There are positive outcomes shown for women with a general increase of female representation in the Fellowship, grant awardees, staffing and career development programmes. However, a continued drive towards gender diversification must be taken to ensure the Academy continues to increase representation.
- Ahead of any other diversity data reporting there needs to be active improvement in the gathering of further EDI data and a more inclusive approach must be adopted to capture this important EDI data to support evidence of change.
Some of the benchmark reporting is limited as many comparable organisations are yet to release diversity data and are still showing 2018/19 reports. Also, as there are no significant changes or movements in the Academy’s diversity outcomes, several factors in the very thorough and detailed benchmarking reporting of 2019 still stand.
Mindful of the lack of movement, a different approach has been taken to benchmarking for this report. Rather than comparing % measures against other similar organisations, there is more focus and commentary on measuring practices in other organisations that the Academy can take initiative or example from to implement into further reporting. The authors chose this approach as there is an obvious lack of diversity representation across the STEM world so reporting that the Academy is a higher % than a comparable organisation could lead to a lack of urgency or complacency.
Currently all data are aggregated to each breakdown (gender, ethnicity, disability etc.). This allows an exploration of each breakdown individually but does not allow for any intersectional analysis (exploring how key activities breakdown over multiple categories). To do this work, the Academy would need the underlying anonymised individual data of the demographic breakdown for everyone. This would allow an analysis to look across categories.