How is an informal transport infrastructure system formed? Towards a spatially explicit conceptual model
Bibliographic record
Abstract
The informal transport infrastructure is an inseparable and critical element of the transportation system in that it provides pedestrian accessibility in planned or unplanned environments. Despite this important role, the informal infrastructure is usually neglected in formal studies, plans, reports or maps. A sophisticated understanding of the different dynamics and mechanisms behind the growth process of the informal infrastructure enables the researchers and practitioners to better plan, conserve and manage open spaces in planned and unplanned environments and helps them predict and manage the growth process of the informal infrastructure in the context of historical cities or informal settlements and model the mutual impacts of infrastructure growth and settlement growth in such areas. In the absence of a holistic spatially and temporally explicit model in the context of GIScience, this research aims to offer an outlook for some of the most important driving forces and aspects of informal infrastructure formation to establish the principal background for developing a spatially explicit, cognitively plausible conceptual model for future research. In this sense, this paper presents a critical review to cover a diverse range of topics in the different disciplines of this area and discuss the theoretical issues on the informal infrastructure formation process to explore, analyze and categorize the role of various human individual and collective-level behaviors and various human and environment interactions in emerging of the self-organizing patterns in the informal infrastructure system.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".