Mongolia, Modernity, Systems + Solutions: Questing Holistic Design + Planning Strategies for a Brighter Tomorrow
Bibliographic record
Abstract
Mongolia is a unique nation underpinned by rich history, spectacular landscapes, rich culture and deep spirituality. It is also a country grappling with change, modernization, growth and governance. The author has been extensively engaged, over many years, in research and consulting in this interesting milieu, including architectural design, city planning, informal settlements and poverty reduction. Building from an innovative integrative framework (Sinclair 2009) for design and planning, the present paper explores the challenges of realizing progress in Mongolia through the lens of systems thinking. In particular, the author critically examines parameters that inform and inspire the development of guidelines to aid in more effective reconsideration, reform and redesign of the urban fabric. A key dimension of the research centers on ethnographic methods, with sensitivities focused on the needs, desires and aspirations of the local community. Many efforts to modernize, advance and develop nations are hamstrung through fragmentation, specialization, narrow agendas and an inability to see the broader picture. The current speculative proposition aims to connect the dots—intentionally pursuing interdisciplinary and interconnected ways of seeing, thinking and acting. While not directly providing answers to questions about the next steps on Mongolia’s path, the author builds and delineates ways of knowing that can support such answers and inform such steps. The main goal of the paper is to consider the complicated ethos in more systemic, holistic, overarching and impactful ways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".