Bibliographic record
Abstract
Abstract Porter and Kramer's “creating shared value” (CSV) proposal has achieved significant penetration into both the academic and corporate communities. Building on other critiques of CSV, this paper assesses whether the CSV framework, notwithstanding its popularity, currently possesses an appropriate and adequate theoretical foundation to represent an overarching normative framework for the entire business and society field. The analysis does so by comparing CSV with a series of other dominant business and society approaches including corporate social responsibility, business ethics, stakeholder management, sustainability, and corporate citizenship. The analysis finds that while CSV does address the fundamental business and society normative requirement that business activities should contribute to sustainable net societal value, it currently fails to adequately incorporate the equally important notions of (i) appropriately balancing stakeholder interests with those of the corporation's shareholders, as well as (ii) demonstrating sufficient accountability (i.e., taking responsibility) by properly reporting on and addressing any negative impacts resulting from the firm's activities. The paper concludes with a revised and expanded restatement of the CSV concept, which attempts to take into account and address its current theoretical limitations in order to enhance its appeal as an overarching business and society normative paradigm.
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 | 0.040 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.047 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".