Research on Comprehensive Competitive Evaluation of P2P Network Lending Platforms Based on BP Neural Network Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Based on many key factors affecting the comprehensive competitiveness of P2P online lending platform that include transaction time, registered capital, popularity points, dispersion points and transparency points, this paper constructs evaluation index system of P2P network lending platform. The 12 secondary indicators of the evaluation system were processed through factor analysis, and BP neural network model was used to quantitatively evaluate comprehensive competitiveness of P2P online lending platforms. The results show that in the selected P2P network lending platform, the highest score of comprehensive competitiveness evaluation is only 0.62 which indicates that the overall competitiveness of China's P2P industry platforms is not strong. The non-equilibrium development pattern of P2P network lending platform seems to be obvious, at least there were regional differences and background differences. Most of the top platforms for comprehensive competitiveness evaluation scores are located in China's first-tier cities or have a background of listed and state-owned capital.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it