Bibliographic record
Abstract
Purpose – The purpose of this paper is to identify the metrics used in the literature to measure social issues in sustainable supply chains. Design/methodology/approach – A systematic literature review was conducted to identify peer-reviewed articles containing metrics pertaining to social issues in the supply chain. A structured content analysis of each identified article was conducted to extract the metrics. This analysis provided a basis for a frequency analysis to determine how often the various metrics appeared in the literature. The metrics were also analyzed to determine whether they: simultaneously addressed the other areas of the triple bottom line, namely, environmental and/or economic issues; were quantitative or qualitative metrics; and could be classified as absolute, relative or context-based metrics. Findings – A total of 53 unique metrics were identified. The analysis of the results showed that a limited number of environmental (3 metrics) and economic (11 metrics) issues were addressed by the metrics as well. A combination of quantitative (39.6 per cent) and qualitative (60.4 per cent) measurements were used. The vast majority of the metrics (90.6 per cent) were further classified as absolute metrics. Originality/value – This paper presents one of the first in-depth analyses of metrics used to measure social issues in supply chains. This is important because social issues are often overlooked in research focused on performance measurement in sustainable supply chains.
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 | 0.043 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.028 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".