Neurodiverse Teams: Conflict and Creativity
Bibliographic record
Abstract
Neurodiversity at work is a rapidly advancing area of DEI which values capitalizing on the range of unique neurological styles within the work group, including both neurotypical and neurodivergent individuals. Arguably, practice in workplace neurodiversity is outpacing empirical research on the phenomenon. Tech firms like SAP, Microsoft, Auticon and Dell are actively recruiting employees on the autism spectrum (Brinzea, 2019; Longmire & Taylor, 2023), and large corporations like Facebook, HSBC, and Hewlett Packard are seeking to harness unique strengths of employees affected by dyslexia or ADHD (Brinzea, 2019). And yet, research on diverse work teams tells us that balancing diverse perspectives, effectiveness, and team cohesion is complicated. Diverse work teams can be creative and innovative (such as top management teams, O’Reilly & Flatt, 1989) and enhance firm performance (Murray, et al., 1989), but subtle group processes determine whether diverse teams have a positive relationship climate (Homan, et al., 2007) versus a stressful and conflictual one (Kößler, et al., 2022). Further, some neurodivergent traits such as alexithymia, autism spectrum, and ADHD come with social perception and expression challenges that tend to escalate interpersonal friction (Brinzea, 2019; Longmire & Taylor, 2023). In this proposed symposium, we will present an up-to-the-minute review of theory and research on neurodiverse work teams, including their mental model of efficacy, creativity, conflict dynamics, and ultimately what supports they need to ensure their individual and collective success. Neurodivergent Team Mental Model: Components and Determinants Author: Michal T. Tomczak; Gdansk University of Technology Author: Pawel Ziemianski; Gdansk University of Technology Team Creativity in Neurodivergent Teams Author: Gamze Koseoglu; University of Melbourne Author: G. James Lemoine; Author: Burak Oc; Singapore Management University Author: Stacey Robin Kessler; Kennesaw State University How Do Alexithymic Traits Predict Perceptions of Conflict in Work Teams? Author: Matthew Hall; Saint Mary's University Author: Debra Gilin; Saint Mary's University Examining Task Conflict Perceptions in Individuals with ADHD Author: Samantha Hancock; Western University Author: Samuel L Plotnick; Western University Author: Sarah Cappellaro; University of Western Ontario Workplace Neurodiversity: A Scoping Review of Team Processes and Outcomes Author: Muhammad Ali; Queensland University of Technology Author: Mirit K. Grabarski; Lakehead University Author: Marzena Baker; Australian Catholic University Author: Maria Hameed Khan; Queensland University of Technology
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".