The systemic tenets of the key supply chain social responsibility approaches
Bibliographic record
Abstract
Social responsibility issues keep reoccurring despite the popularity of numerous approaches perceived widely as adequate. In this paper, the authors conducted a systematic literature review to explore this phenomenon from a systems thinking standpoint. The findings revealed that each approach is founded on a different systemic paradigm, makes different assumptions on the nature of social responsibility issues, and has different objectives when resolving them. Therefore, employing any of these approaches alone will certainly fail given their underlying systemic limitations. The findings also revealed that these approaches are complementary from a critical systems thinking perspective, hence, researchers and practitioners can use their tools and methods together in the form of tailored interventions to better address efficiency, subjectivity, and fairness when resolving social responsibility issues. This paper concludes by proposing a practical framework based on critical systems practice which encompasses four systemic paradigms allowing the inclusion of a spectrum of perspectives, and assumptions.
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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 | 0.061 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".