Index
Bibliographic record
Abstract
Black spaces finding opportunities in, 155 Bling H 2 O, 111 Blockbuster video, 193 Blu-Ray, 26 Bone implants, 209 Bono, Edward de, 207 Borders, 76 Boston Red Sox, 120 Boston Scientific, 155 Botox, 93 Botticelli, 70 Bottled water, 98, 111, 208 Boucheron, 81 BP (British Petroleum), 220 Braces, Invisalign and, 156, 165, 166, 168, 170 Brainstorming, killer ideas and, 204-205 Brand expression: dimensions and, 108-109, 113-114 relationships tensions and symmetries, 76, 77, 83 as source of opportunity, 54-55, 64-65 Brand growth, 113-114 Branson, Richard, 35, 114 Brazil, 66, 146, 225 Bright Science, Brighter Living, 181 British East India Company, 162 British Empire, 226 British Parliament, 232 British Petroleum (BP), 220 Broad, Eli, 22 Broadcast planting, 191 Bunge, 137 Burj al Arab, 65 Burt's Bees, 159 Business leaders, vision needed for future, 234 Business models: for-benefit model, 37 change and, 96 environment and, 83-85 generation, 54 opportunities and, 40, 72, 108-112, 141, 147, 155, 201, 208, 219, 225 opportunity shapers and starters, 151, 159, 162, 164-166, 170, 193-194 relationship tensions and symmetries, 76-77, 83 as source of opportunity, 54-55, 62-63, 64 value creation and, 51 Business organizations, vision needed for future, 235
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 | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.862 | 0.860 |
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".