Architecting Optimal (Space) Strategies: A Quantitative Framework
Bibliographic record
Abstract
The harsh light of the explosion of a full-scale Russian invasion of Ukraine illuminated the strategic importance of space technology, making it clearly visible to nations around the globe. Space technology has become omnipresent over the past decades thanks to new technologies and lowered barriers of access to space as well as the expansion of services relying on satellite data. While the barriers of access may be lower, the acquisition of effective space capabilities remains a costly and challenging task for emerging or aspiring space actors. Yet literature on and tools for measuring the outcomes of national space investments, space capabilities or designing impactful space strategies remains limited. This thesis contributes a series of methods to advance the field of quantitative space strategy development, with implications for industrial policy in other domains. First, it presents the results of an in-depth econometric panel data analysis based on one of the most comprehensive space-capability datasets to date. Twenty-six outcome variables, measuring various dimensions of space capability, are evaluated for nine space actors over ten years, drawing on a wide range of sources, including fieldwork conducted in the Asia–Pacific region and the UK. Statistically significant relationships are identified, including those between the operational age of a space agency and per-capita space spending, and between spending levels and astronaut flight frequency. Second, the thesis introduces two novel metrics for comparing space capabilities. One metric, derived via unsupervised learning, combines the variation of all 26 indicators into a single quantitative “spacepower” value for each actor. The other, a non-dimensional index named CPPI, quantifies the capability provided by individual space assets. Example scores range from 47 for a smallsat to 75 for a large geostationary satellite. Finally, the thesis proposes two network-science-based methods to help analysts and decision-makers understand the structural origins of strategic success. The first method examines how the network centrality of a nation’s space manufacturing sector correlates with the output impact of spending. The second method introduces a state-of-the-art agentic-flow tool, StratAgeM, which builds a network topology of a strategy based on a specialised ontology. A strategy’s “connectedness”—quantified via the spectral gap of its architecture—hints at a relationship with performance in capability development, with values of approximately 0.23 for Canada and 0.3 for Korea, based on their 2012–2013 strategies. The domain-focused contributions include one of the most detailed quantitative analyses of space capabilities to date, alongside new metrics to compare national space trajectories. Several of the methodological contributions—–particularly those grounded in agentic flow modelling—extend beyond the space sector.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".