Assessing the benefits of serious games to support sustainable decision-making for transboundary watershed governance
Bibliographic record
Abstract
The overarching objective of this paper is to consider the ways that serious games could be developed in the future to maximise potential benefits in the context of governance of complex transboundary water systems. A focus is placed on the use of serious games for decision-making; as such the use of serious games for other purposes (e.g. games with a solely educational purpose), while interesting and useful, is outside the scope of this work. The Upper St Lawrence was used as a case study of a transboundary watershed to enable specific examples of the potential uses of serious games to be drawn. A review of the serious games and decision-making literature was undertaken to derive a theory framework for the way serious games might be able to support decision-making in different contexts. Following this, exploratory interviews were conducted with water resource managers across the St Lawrence region to establish whether a serious game might be useful in this particular context. Three decision-making contexts were identified in which serious games might be a useful tool: decision-making involving complex systems or significant uncertainty, decision-making involving multiple stakeholders with divergent perspectives and decision-making under time constraints. It was found that several contextual factors make the St Lawrence region a potentially viable candidate for the development of a serious game to support decision-making.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".