Call for Papers: SPECIAL ISSUE Business Models at the Crossroad of Responsible Innovation, Sustainability and Resilience
Bibliographic record
Abstract
CALL SPECIAL ISSUE The relentlessly fluctuating global economy generates impelling needs in how values are perceived, created and managed. Recently, there has been focus on how businesses could be part of the solution of our rising common challenges, such as climate crisis, general pollution, poverty, energy security and health. Business models are the core of businesses and support companies' effectiveness, contributing to their stable, sustainable functioning in the difficult, ever-changing market. This implies the design and implementation of innovative business models that take into account the variety of the stakeholders and promote contributions in terms of responsible research and innovation (RRI), sustainability and resilience. This Special Issue aims to discuss the key mechanisms concerning the design and operationalization of business models as a tool to meet our global challenges. We welcome contributions that links concepts such as sustainability, resilience and RRI to business models and strategy. Submission Deadline: January 30 2020, February 20 2020 Review Process Ends: April 30 2020 Special Issue published: July 2020
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.601 | 0.474 |
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".