Using facilitative skills in project management
Bibliographic record
Abstract
Project management can be seen as a profession, discipline and conceptual framework. It has been developed from different fields, including military engineering, mechanical engineering, social sciences and construction. During recent decades, there has been a number of challenges as to its efficacy, for example disappointing project performance and lack of an appropriate project cooperation method due to new forms of cooperation possibilities. More and more organizations are engaged in contractual joint ventures, alliances and other forms of inter-organizational relationships. In addition, virtual cooperation, mediated by interconnected and diversified systems, is becoming more and more common. These relatively new forms of interaction imply new demands on skills and methods facilitating project cooperation within and among various organizations. Given the pervasiveness of these demands, project managers are frequently finding themselves in situations where using facilitating skills is not an option, but a requirement. Facilitation is to be viewed as a process of ‘obstetric’ aid to meet the challenges of coping with the changing conditions for project management described briefly above. The outcome of facilitation depends on at least four interrelated sets of conditions: a) The available time and resources in comparison to the complexity of the aim(s), b) the composition of the participants, c) the skills of the facilitator and d) the methods available to the facilitator. In this paper facilitating skills are identified and discussed in relation to the changing circumstances for project management. The approach used to achieve this paper’s objective includes a literature review, model building and reflection on facilitation skills based on the author’s experiences from facilitating workshops for company managers, public administrators, NGO’s and university professors / students around the world. In addition, this paper is based on the author’s many years of experience in supervising engineering students from for instance China, South Korea, Canada, US, Ghana and various European countries who have come to learn and practise facilitating skills as international students at Technical University of Denmark. The paper identifies facilitation skills at three different levels: the intellectual, emotional and synergistic level. An analysis is conducted based on a practical example of how engineering students are able to learn facilitative skills. The contributions of this paper to the field are an extension and a deepening of existing knowledge of facilitation skills at different levels. In addition, the paper includes a model regarding effective ways of combining various ways of knowing in a facilitation course for university students and future project managers.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".