Perspectives on Diversity in Knowledge Management Research
Bibliographic record
Abstract
Novel ideas and innovation have been known to flourish at the intersection of disciplines, industries, cultures, and more. Actively seeking these points of intersection can catalyze novel patterns of thought, fresh interpretations, and radical transformation. Diversity therefore can be considered a core prerequisite for innovation. Diverse ideas support testing of solutions outside the norm, exhibit heightened creativity, logical reasoning, error-detection capabilities and consistently demonstrate superior performance compared to homogenous ones. Despite the benefits of diverse teams heralded in the business world, not enough attention has been paid to a reflective examination of the inclusivity practices within the field of knowledge management itself. Whereas the existing diversity literature has predominantly explored this phenomenon through lenses such as race, gender, and ethnicity, a broader understanding is imperative. Studies within Library and Information Science (LIS) education have aimed to enhance services for diverse clientele and increase diversity among professionals. Similarly, research in STEM fields has shed light on the barriers faced by women scientists throughout their careers. Biases in medical education, such as the use of color-blind illustrations when teaching about skin conditions, have also been scrutinized. Moreover, the COVID-19 pandemic has starkly exposed prejudices in healthcare treatment and knowledge dissemination, emphasizing the urgency to broaden participation and incorporate diverse perspectives. This chapter explores diversity in knowledge management research based on seven attributes including: geographic location of authors, collaboration patterns, frequency of the term diversity in document titles, language of the work, accessibility of journals, departmental affiliation of contributing authors, and composition of the editorial boards of each journal. While this list is not exhaustive, these attributes contribute to the beginnings of a rudimentary diversity checklist for journals in knowledge management research. Our intention is to provoke discussion and further exploration and questioning of the structures, norms, and gatekeepers of the research enterprise.
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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 | 0.078 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.014 | 0.103 |
| Scholarly communication | 0.037 | 0.036 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.008 | 0.013 |
| 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".