Revisiting the Use and Utility of Domain Mapping: A Comparative Study of the Future(s) of Diplomacy and International Affairs
Bibliographic record
Abstract
ABSTRACT Domain mapping is a necessary yet too‐easily overshadowed component of strategic foresight. A domain is defined as any topic that serves as the central focus of a foresight project. Domain mapping is the process of conceptually framing its scope, often by way of participatory brainstorming sessions with subject matter experts. Domain mapping should be thought of as a prerequisite to robust foresight research, a crucial and necessary preliminary step that animates all subsequent processes. Our article has two objectives: to reinvigorate the discussion on the use and utility of domain mapping by illustrating how, why, and when to use the technique; and, using a series of nine domain maps created with hundreds of Canadian public servants between 2018 and 2024, to test a novel approach for empirically evaluating the cumulative results of domain mapping by comparatively assessing the thematic shifts policy practitioners have attributed to the future(s) of diplomacy and international affairs. Our approach illustrates how longitudinal empirical studies of domain maps can shed light on the emerging and shifting perspectives of foresight experts and policy practitioners. The paper highlights nine separate uses for the technique, identifies where foresight and domain mapping are currently used within the Canadian government, analyzes an original set of related domain maps, and provides lessons on facilitating, using, and applying domain mapping with a focus on representation, group dynamics, and data quality.
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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 | 0.024 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".