Knowledge construction and risk induction/mitigation in dialogical workgroup processes
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how knowledge is constructed and risk is induced within the workgroup environment of a large North American aerospace company. Design/methodology/approach Based on an epistemological position on knowledge and risk, an initial conceptual framework is proposed. This is then evaluated and re‐constructed across a qualitative and ethnographic case study approach involving direct observations and interviews, whereby empirical results were interpreted and analysed across discourse analysis. Findings A dialogical model is proposed describing both verbal and non‐verbal interactions between group members leading towards knowledge complexification on the one hand and risk mitigation on the other hand. Factors leading towards dialogical breakdown and subsequent risk induction are also presented. Research limitations/implications This single case study prevents generalizing the findings across the entire firm in question, and by extension any manner of external validity outside of the firm's context. Additional workgroups/teams within the firm need to be evaluated, while similar studies in other institutions within the knowledge economy are to be envisaged. Practical implications Workgroup managers must nurture an environment conducive towards mutual trust and respect, where individuals are given the time and freedom to express themselves, all the while being open to differing viewpoints and experiences. Coercive dialogue between members should be discouraged. It is proposed that this can be achieved across a parental “safety net” approach. Originality/value The paper presents the “how” and “why” of an effective dialogical knowledge constructing process occurring at the interpersonal level, attempts to propose how management can to help achieve this within their organisation, and attempts to bridge the areas of knowledge creation and risk induction at the interpersonal/workgroup level.
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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 | 0.023 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.002 |
| 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".