Assessing foresight to advance management of complex global problems
Bibliographic record
Abstract
Many people do not like thinking about the future. If they do, over 50% of Canadians think “our way of life” (p. 7) will end within 100 years and over 80% of Canadians think “we need to change our worldview and way of life if we are to create a better future for the world” (Randle & Eckersley, 2015, p. 9). There is a good reason for this. Alarms have sounded over global urgent complex problems with potential for catastrophic consequences such as the development of artificial intelligence, climate change, mass extinction, nuclear war and pandemics (Marien & Halal, 2011). Society is also increasingly fragmenting as imminent crises build on lack of understanding, the sense of incapacity to act, fear, distrust, blame and a lack of hope. This struggle for humanity’s survival is complicated by the turbulent global environment in which institutions continue to follow path-dependent trajectories set forth in a different time and context. Governments at various levels face a problem of “fit” between current structures and processes, that have not progressed sufficiently to meet changing needs of a global society mired in complexity and governance challenges. However, hope exists. Incremental progress on many fronts and a massive amount of efforts and resources are being engaged worldwide. There are emerging fields, lenses and tools that can potentially alleviate complex problems and address this emergency. The purpose of this dissertation is to understand and assess dialogue-based foresight practices being applied towards complex problems in Canada to provide insights into how these practices can assist society to alleviate global urgent complex problems and their impacts, within this backdrop of looming crises. Foresight, alternatively known as future studies or scenario-building, is a forward-looking practice recognized and used globally with over 100 research organizations focused on foresight, widespread usage by firms and over 18 countries involved in foresight activities (Berze, 2014b). Overall literature findings suggest foresight is widely and at least incrementally effective with a number of impacts in various areas (Calof, Miller, & Jackson, 2012; March, Therond, & Leenhardt, 2012; Meissner, Gokhberg, & Sokolov, 2013) but the extent of this effectiveness, the mechanisms involved, and the specific foresight benefits per type of project needs further research and evidence. For instance, limited literature exists on whether foresight can transform complex situations and if so, under what conditions. Thus, opportunities exist for assessing and increasing foresight’s impact. This dissertation is a contextualized, systematic empirical study that taps into transdisciplinary literature and practice, case studies of how foresight has been used to address specific types of complex problems in Canada, as well as surveys and interviews with foresight experts and participants. This dissertation uses a foresight community scan and a comparative case study approach to provide practical and theoretical benefits to foresight and complex problem area stakeholders. The research focuses on studying the broad interactions of foresight and identifying the impacts of dialogue-based foresight projects on people and the outcomes of complex problems. The dissertation concludes that dialogue-based foresight is a valuable and unique practice for ameliorating complex problems and their consequences. Insights are offered towards dialogue-based foresight’s potential contributions within the context of other efforts directed at humanity’s struggle for survival and global complex problems. These insights can then foster the further development and application of dialogue-based foresight on a global scale to alleviate complex problems and their effects. The dissertation outlines recommendations on key next steps to realize these potential contributions.
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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 | 0.029 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".