Model for Building a PropTech Ecosystem Through a Network of e-Platforms and Software Service Integration
Bibliographic record
Abstract
The present article examines the conceptualization of a model for building a digital business ecosystem through design and implementation of an e-platforms’ network that integrates software services within PropTech Bulgaria as an international organization. “Business Model Archetypes” as a conceptual framework is being applied to the real business case of PropTech Bulgaria, thus clearly showcasing how different business models (BM) correspond to respective e-platforms starting from the basic BMs like product, service, and commerce; developing into combined BM forms such as subscription, marketplace, and brokerage. The system architecture is implemented through a modular approach, led by the understanding that strategies, knowledge, and technologies change over time. As a natural consequence, transaction points change as well. Having a modular structure presupposes a bigger number of transaction points. Currently, there is no communication among separate modules through data transfer. The study advances a practically applicable model for developing a resilient international PropTech ecosystem, i.e., the most complex business model archetype.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".