Bibliographic record
Abstract
hyperactivity disorder, 40 Attribution theory, 156-157 Augmented reality (AR), 130 Autonomy, 113 Axial coding, 22-23 AXIS FLIP, 135 Big data, 137 Bivariate analysis methods, 161 Bivariate correlations, 182 Body scan, 41 Bolivia, 14-15 Born Global, 58-60 Boston Consulting Group (BCG), 135 Bottom-up strategy process, 78 Breathworks, 40 Bullock and Batten (1985) phased approach, 150 Capability, 96 Categorisation, 39 Change, 152 models, 150 ontological considerations of EDI, 154 and performance, 153 step and phase models, 152-153 Changing, 152 Chartered Management Institute (CMI), 46 Climate, 9 Cloud-based tracking dashboards, 136 Commitment, 85 dilemma with reciprocity concerns, 93-94 Commodity Chain, 114 Compassion, 41 Concentration, 44 Confirmatory factor analyses, 182 Conformity assessments, 14 Context-oriented approach, 10-11 Controllability, 156 Correlation, 164-166 COVİD-19 pandemic, 77, 175 Creating-being mode, 44 Credibility, 96 Cronbach's alpha, 161 Cross case analysis, 86-91 Cross-cultural management, 102-103 culture, 103-106 global value chains, 114-118 interculturalism and, 106-114 Cultivate, 136 Cultural compatibility, 27-28 Cultural diversity, 61-62, 68 Culture, 9, 61, 102-103 emergence, definitions, and meaning, 103-106 CVS Health, 134
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.768 | 0.771 |
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".