Bibliographic record
Abstract
If we observe top companies in any industry, we notice they have one thing in common: innovation. Successful business leaders recognize when the same ideas and methods used before aren’t working anymore. Smart, innovative approaches are needed for our hybrid working environment. The ABCD business model shows that present organizations spend the majority of their time on activities related to business administration (A) and doing repetitive work (D). The rest of the time is allocated to dealing with crises (C), and only nominally to improving the ways business is done (B). Digital transformation, competition, and the need for organizations to leverage technology and innovation in the future will ‘force’ organizations to maintain A, increase B, and (strategize how to) decrease C and D. Two initiatives will be unpacked and common elements will be identified as indicators in improving B. Five ways to change the game and become a human-focused organization that promotes innovation are proposed based on our learnings: People: Encourage a growth mindset of continuous learning, creativity in how problems are solved, and flexibility how work gets done Encourage innovative thinking; create innovative groups Build skills, e.g., analytical thinking, innovation, creativity, and initiative Workplace: Design a psychologically safe culture, where people are included, can learn, have a sense of belonging, are appreciated, and valued for who they are and what they contribute and challenge. Technology: Create an experimentation lab to TRY-TEST-ADAPT in rapid cycles to learn and fail/learn fast or advance the innovation. We are faced with multiple, messy issues that require out-of-the-box thinking and innovative solutions. Capturing lessons learned can build leading indicators that will help improve B. A simulation dashboard that quantifies the change is an innovation tool we plan to develop.
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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".