Bibliographic record
Abstract
performance, 123-124 Adjusted return (AR), 135 Artificial intelligence (AI) method, 24-25 Artificial neural network (ANN) model, 28-30 artificial intelligence (AI) method, 24-25 in-sample performance, 39-44 model specification, 38 out-of-sample risk forecast, 44-49 ASEAN Economic Community (AEC), 150-151 Asset pricing, 67 Autoregressive conditional heteroscedastic (ARCH), 27-28 Autoregressive moving average, generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model artificial neural network (ANN) model, 25, 27-28, 46-49 in-sample performance, 39 root-mean-squared-error (RMSE), 39 samples, 32-33 The Basel Committee on Banking Supervision, 2019, 26 Bid-ask spreads, 3-4 Black box hypothesis, 202 Black-Scholes option pricing formula, 53 Bloomberg Barclays US Aggregate Bond Index, 31 Body mass index (BMI), 182 Bond analysis, 67 Bond momentum, 71 excess returns, 90-91 liquidity, 66, 87-88 longer holding periods, 76 rating, 73-74 subperiods, 84-86 Trading Reporting and Compliance Engine (TRACE), 75 Buy-and-hold returns (BHR), 134-135 Capital structure theories pecking order theory (POT), 151-152 trade-off theory (TOT), 151 Cash holdings, 104 Category-based thinking, 3 China stock market and Accounting Research (CSMAR), 8, 11-12 Chinese economy, 201 Christoffersen's test, 35 Closed-form option pricing formula model setting, 55-56 moment generating function, 57-58 option pricing formula, 56-60 Component-driven regime switching (CDRS) models, 54-55, 61-62 Computable general equilibrium (CGE), 215 Constant Difference of Elasticities (CDE), 216 Constant elasticity of substitution (CES), 216 Controlling shareholders corporate governance, 107-109 firm attributes, 105-107 market condition, 107
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.723 | 0.759 |
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".