Bibliographic record
Abstract
133 Akaike's information criterion (AIC), 94 Alexander Bathory model, 220 Alpha Cronbach measurement, 117 Altman Z-score assessment, 153 Area of adaptation (Area A), 126 Area of development (Area D), 127 Area of stable growth (Area R), 127 Area of survival (Area S), 126 Artificial intelligence systems, 38 ASEAN Collective Investment Scheme (2014), 42 ASEAN Service Liberalization Agreement, 92 ASEAN-China Free Trade Area (ACFTA), 132 Assets, 38 diversification in investment activity, 102 management company, 38 prices, 43 pricing model, 55 Assets under management (AUM), 43 Association of Investment Management Companies, 43 Association of Southeast Asian Nations (ASEAN), 89, 132 Auditing, 87 financial statements, 3 Augmented Dickey-Fuller unit root test (ADF unit root test), 94 Autoregressive distributed lag (ARDL), 87 bounds testing, 93 limit testing method, 93 Average Variance Extraction (AVE), 75 INDEX British investment funds, 41 Budgets financial literacy, 117 Buffet indicator, 104-105, 108 Buffet ratio, 107 Business, 193 operations, 204 sectors of trade, 90 services, 97 Canadian hybrid funds, 41 Capital asset pricing model (CAPM), 55 Capital market, 102 Capital structure, 205, 218 Caribbean Community (CARICOM), 23 Central African Economic and Monetary Community, 24 CEO Compensation (CC), 9-10, 14 China Stock Market and Accounting Research (CSMAR), 207, 222 Chinese equity funds, 41-42 Chinese real estate enterprises, 218-219 Classical theory of wave evolution, 124 Cluster(ing), 44, 46 analysis, 39, 43 performance, 39 Co-movement of real exchange rates, 22, 24 Cobb-Douglas production function, 92 Cochran's formula, 71 Cointegration, 94 results, 94-97 Commercial banks, 42 Committee for Economic Development, 2 Communication, 115 Computation of mean scores, 73 Computers, 38, 193 Conditional CAPM, 55 Confirmatory Factor Analysis (CFA), 74-76 Conscientiousness, 70, 72 Conservatism, 206 Construction, 87
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.717 | 0.743 |
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".