Bibliographic record
Abstract
Absolute poverty, 245-246 Access to finance, 412 Account inactivity ratio (AIR), 419 Account ownership of any type, 379-380 in financial institution, 380 Account usage ratio, 419 Accounting practices, 87 during pandemic, 89-91 Accounting-based criteria, 134 Acquisitions, 434-435 Ad hoc model, 185 Affective commitment, 58 Affective organizational commitment (AOC), 51, 57-58 (see also Normative organizational commitment (NOC)) and KSB, 59-60 reciprocity and, 58-59 Akrah, 347 Amana Funds, 354 Antakya Long Bazaar, 153, 155-156 Anthropomorphism, 355 Appraisal, 280-300 Arduino, 369 Arellano bond estimation, 32, 41 Asia Pacific Energy Research Centre (APERC), 181 Asian financial crisis, 278 Asset Management Company (AMC), 300 Assets in funded private pension plans and public pension reserve funds, 25-28 Association of Southeast Asian Nations (ASEAN), 184 Asymmetric impact, 320 Asymmetry, 322, 325 Atmosphere, 268, 271 Attitudes, 218 use of crypto currency, 218-220 Autoregressive Distribution Lag (ARDL), 299 Autoregressive distribution lag approach (ARDL approach), 320, 325 CUSUM stability test results, 339 result post-crisis period, 332 Autoregressive fractionally integrated moving average (ARFIMA), 323 Autoregressive Integrated Moving Average (ARIMA), 299 Ave Maria Catholic Values Fund (AVEMX), 352 Average variance extracted values (AVE values), 229, 450
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) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.759 | 0.803 |
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".