Measuring social issues in sustainable supply chains
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
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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