Real-Geographic-Scenario-Based Virtual Social Environments: Integrating Geography with Social Research
Bibliographic record
Abstract
Existing online virtual worlds, or electronic environments, are of great significance to social science research, but are somewhat lacking in rigour. One reason is that users might not participate in those virtual worlds in the way they act in real daily life, communicating with each other in familiar environments and interacting with natural phenomena under the constraints of the human–land relationship. To help solve this problem we propose the real-geographic-scenario-based virtual social environment (RGSBVSE). The aim is to enhance the ability of current virtual worlds in social issues studies by promoting virtual geographic environments that are built with real scenarios in the physical world. In this paper we first discuss the potential shortage of current virtual worlds for serious social research. We then explain how real geographic scenarios can contribute to building a virtual social environment by providing (1) real geographic data, including the time dimension, in terms of data acquisition and organisation; (2) dynamic or real-time natural phenomena and processes for scenario simulation and expression; (3) shared spaces that enhance participants' interaction through a mix of virtuality and reality; and (4) shared hot spots of social phenomena for researchers from multidisciplinary (eg, sociology, psychology) performing collaborative research. Furthermore, two of our projects, the virtual Chinese University of Hong Kong and the Virtual Globe of the Chinese Family Tree, are introduced as case studies, to illustrate how the RGSBVSE can play a significant role in a number of critical social research issues from the local to regional scale.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| 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".