Bibliographic record
Abstract
Many commentators suggest that the basis of management is (1). But, when the management problem covers more than one organization, and those organizations are of different sizes or have different core competencies, there is good reason to believe that they have different cultures and, consequently, sense is not necessarily common (2). Based on our studies of 13 Japanese and Canadian firms and their partners, it seems clear that when dissimilar organizations work together, they need concrete collaboration skills to guide their actions. Specific collaboration skills, which can be taught, are necessary in this delicate and sensitive aspect of management. To understand the role and importance of these skills, we suggest a metaphor from another activity that requires sensitivity and delicacy: dancing. The process of learning to is our metaphor for learning to collaborate for technological innovation. We call the organized, deliberate (but fun) process of acquiring these skills dance lessons. The metaphor is intended as a heuristic, a better way to understand the steps that an individual or an organization takes as it learns to be a better collaborator. The metaphor can help us to recognize why and when training in technological collaboration is appropriate. Why Dance Lessons? One reason why a firm might seek to increase its skills in technological collaboration would be to put more into its collaborative activities. The potential from the collaboration (here, the prospect of developing hot new technology), can actually be a significant motivator. There is a small but growing literature on the role of humor and fun in the workplace (3-5). Although not without its critics (6, for example), the beneficial role of fun in management training seems to be gaining credibility. One of the reasons for this is that in a learning environment, humor has the ability to break down barriers and increase learner involvement and information retention. Another explanation is that technical people find fun in learning new skills and they seek out projects or employers that provide plenty of opportunity to so. It doesn't hurt that in a dynamic, technology-driven economy, those new skills are highly marketable. Given the great cultural and core competence divides that may separate innovation teams engaged in collaboration, the ability to surmount resistance to change is an important aspect of any training program. For this reason, we suggest, there may be more than metaphorical importance to the use of the dance concept when deploying skills training for technological collaboration. It may be that tim, as in dancing, becomes the mechanism for inspired-not merely acceptable-performance. Other reasons include learning how to lead, how to follow, how to communicate, how to select the partner (customer, supplier), how to build trust, how to manage risk and avoid danger, how to negotiate with a partner from a different culture, how to communicate effectively and efficiently, how to share risk and benefits, how to collaborate for sustainability, how to make collaboration sustainable and how to collaborate on the global stage. A thread in these reasons is the focus on having the skills needed to make choices before collaborating. When Are Lessons Appropriate? We believe firms should consider taking dance at any time. Instead of initiating training immediately before starting to collaborate or negotiate on an issue, we suggest a firm consider lessons as early as possible. While there is merit in postponing training in cross-cultural communication until one is about to travel on the assumption that this just in time training will be fresher and more relevant, training for technological collaboration is presumably about more than doing things right in an existing or established plan. Instead, training should include significant attention to the selection of partners and projects, or doing the things. …
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.036 | 0.014 |
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".