Improving Equity, Diversity, and Inclusion in LIS Education: Insights for Curriculum Development and Professional Preparation
Bibliographic record
Abstract
A Review of: Drewry, C., Matsuno, S., Hicks, A., & Inskip, C. (2024). How could the Library and Information Studies curriculum better prepare graduates to address equity, diversity and inclusion issues in their workplace? Journal of Information Science. Advance online publication https://doi.org/10.1177/01655515241245960 Objective – To explore how the graduate-level Library and Information Studies (LIS) curriculum can be redesigned to be more socially justice focused and thus better prepare graduates to address equity, diversity, and inclusion (EDI) issues in their workplace. Design – A cross-sectional, descriptive online survey study. Setting – MA/PG Diploma program in LIS at University College, London (UCL). Subjects – 59 recent graduates from the MA/PG Diploma program in LIS at University College, London (UCL). Methods – Using the descriptive survey methodology, a 13-item online questionnaire was sent to a purposive sample of 733 alumni from the MA/PG Diploma program in LIS at University College, London (UCL). The online survey included 7 closed question and 6 open-ended questions, and was open for 6 weeks. Survey responses were analyzed using thematic coding in NVIVO software to identify key trends and insights. Main Results – Regarding effective pedagogical strategies for EDI-focused work, a notable theme was the importance of personal identity in understanding and engaging with EDI issues. Respondents mentioned that their own experiences of marginalization, promotion to management roles, and personal study, helped them recognize the significance of EDI in their professional lives and understand the broad array of protected characteristics in their EDI work. Group work and community building were also identified as crucial for effective EDI education. Respondents noted that working collaboratively, both in professional associations and with colleagues, helped them maintain motivation and deepen their understanding of EDI issues. Workshops, discussion groups, and online forums were highlighted as valuable tools for fostering these connections and promoting shared understandings. Another key theme was the need to embed an EDI ethos throughout the entire curriculum rather than isolating it in specific modules. Respondents advocated for integrating EDI principles into all aspects of LIS education, including lectures, reading lists, and course content to ensure a holistic approach. Gaps in the curriculum were also noted. First, there was a lack of training in management and leadership, particularly in areas like inclusive hiring practices and managing diverse teams. Respondents felt unprepared to address these practical challenges, which are critical for nurturing a diverse and equitable workplace. Second, fostering learner positionality needs to be strengthened in the curriculum. Positionality refers to how differences in social position, identity, and power dynamics shape individuals' experiences and access to opportunities. Without developing an understanding of these dynamics, students may struggle to fully grasp the complexities of marginalization or may inadvertently impose their perspectives on others. Lastly, respondents highlighted the need to broaden the scope of EDI education to address all protected characteristics under UK law, not just race and ethnicity. Conclusion – Three effective pedagogical strategies and three curricular gaps were identified to help LIS graduate programs to improve their EDI-focused curriculum. Specific approaches such as embedding EDI throughout the curriculum, encouraging students to reflect on their own identities and experiences with marginalization, and promoting collaborative activities were recommended. In the process of curricular form, educators need to be mindful about the tensions related to the pressure placed on those from marginalized communities to share their experiences and lead EDI work, challenging existing structures, and performative diversity. Lessons from archival practices can be considered, such as adopting trauma-informed practices when engaging with communities that have experienced historical or ongoing harm, and shifting towards more relational and person-centered approaches to build relationships with diverse user groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".