Bibliographic record
Abstract
best practices, 136-137 cancer hospital, 131 conceptualisation and foundation, 126-127 concern for community for sustainable community development, 122 COVID-19 protocols, 133-134 diagnosis at, 132 diversified services, 130-131 doctors and medical facilities, 128-129 economic contribution/perspective of Sayeed Ahmad Ansari and brothers, 135 hospital profile and model of operation, 125-137 infrastructure of HCG, 131-132 location and overview, 126 management model, 129-130 management of hospital, 128 managerial implications, 136 Medanta ARAMW Blood Bank, 133 medical oncology, 133 members and non-members loyalty, 127-128 methodology, 125 nuclear medicine, 133 objectives, 125 oncology, 133 principles of cooperatives in healthcare, 121-122 profile of founder, 125-126 review of literature, 122-125 social perspective, 135-136 socio-economic impact, 134-135 surgical oncology, 132-133 sustainable competitive advantage of Medanta ARAMH, 137 treatment at, 132 Weaver's Hospital, 3, 68 Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV), 170 Accessibility, 159 Accommodation, 130 Affordability, 17, 140 Africa, healthcare cooperatives in, 64 Amul, 56-59 Annual General Meeting, 87 Anti-fragile, 49 Aravind Eye Care Hospital, 57 Argentina, 95 constraints, 111-112 impact of cooperatives in general and healthcare cooperatives in particular on women's lives, 107-109 distinguishing features of Argentina healthcare cooperatives, 102-105 examples of healthcare cooperatives in, 109-111 healthcare cooperatives in, 65, 94-95 healthcare in Latin America, 95-102 women in, 105-106 women in cooperative movement in Argentina, 106-107 Argentina Cooperative Movement, 65 Argentina Trabaja, 109 Artes Gráficas Patricios, 110 Index
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.774 | 0.815 |
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".