Digital platforms: Wrestling with the sustainability design challenges
Bibliographic record
Abstract
During an executive meeting, the senior vice president of a large technology firm discussed recent sustainability developments with the managing director of a global implementation firm. They concluded that sustainability is gaining traction and significantly impacts data collection, analysis, and reporting. They agreed to jointly invest in developing a sustainability module to integrate into the technology firm’s digital platform. By reaching out to a client interested in becoming a “launching customer,” they established a digital platform ecosystem and created the sustainability module. This case outlines the real design challenges faced by the ecosystem partners. Seven (7) design challenges have been identified, ranging from selecting and importing data tracking metrics against goals and targets to creating a dashboard. Three environmentoriented features (e.g., decarbonization, travel emissions, energy consumption) were launched as a minimum viable product and rolled out to the client. This teaching case consists of two parts: the first part introduces the concept of sustainability, digital platform ecosystems, a case description, and the design framework, while the second part discusses the seven identified design challenges.
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".