The spatial representation of business models for climate adaptation: An approach for business model innovation and adaptation strategies in the private sector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Climate change adaptation requires organizations to recognize the numerous natural and social dimensions of climate risk. In the private sector, local adaptation responses to climate change are observed as changes to the limited capacities of organizational processes to plan for social aspects of adaptation. This article applies a methodology to map these connections and presents empirical evidence of a firm's autonomous adaptation measures along their supply chain in Baja California, Mexico. The spatial conceptualization of the business model illustrates the potential to identify sources of climate‐related risks, autonomous adaptation actions, and the barriers to improving the feedback loops to facilitate the integration of local knowledge for business model innovation. The results suggest that coproduction of innovations is a mechanism for organizational learning that can help to overcome the challenges for business strategy to identify the wide array of local factors associated to climate adaptation and normalize adaptation planning into business models. This approach might accelerate leveraging the capabilities of the private sector for socially oriented forms of adaptation that amplify the transformational value of business model approaches for improved adaptation strategies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it