The current state of integrating equity, diversity and inclusion into knowledge mobilization: a systematic literature review
Bibliographic record
Abstract
Purpose The purpose of this paper is to systematically review and analyze the academic literature on integrating equity, diversity, and inclusion (EDI) into knowledge mobilization (KMb). Design/methodology/approach This systematic literature review of the body of scholarly literature published on integrating EDI with KMb follows established methods and protocols proposed by Popay et al. (2006) and Page et al. (2021). Using a relevant keyword string, a search was conducted in ProQuest and SCOPUS to find peer-reviewed articles, which were then screened using predetermined inclusion and exclusion criteria. Finally, inductive and deductive analyses were conducted on the selected articles. Findings The findings suggest that most of the authors are based in the Global North, the majority of literature was published in the last two years, and that it is conceptual. This synthesis highlights five solution-oriented themes: acknowledging inherent bias, centering marginalized groups, promoting responsible knowledge mobilization, establishing partnerships, and advocating for transformational and systemic change. This study also identifies four broad barriers: inherent, unconscious, and implicit biases, a lack of evidence-based best practices, siloing of research and information, and a lack of institutional support and resources. Findings also highlight the value of further research into barriers, gaps and opportunities. Originality/value By studying the intersection of EDI and KMb, this contemporary synthesis of the state of the field presents opportunities for future research into gaps, barriers and potential solutions.
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 | 0.110 | 0.271 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.036 | 0.032 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".