Applied qualitative methods for social life cycle assessment: a case study of Canadian beef
Bibliographic record
Abstract
This paper presents a transparent and rigorous methodological approach to conducting a case study of social performance (SP) within the conventional life cycle of Canadian beef. The objective was to explore the potential benefits and risks (or hotspots) of practices on Canadian beef farm businesses (BFBs) and slaughter operations (BSOs) to establish valid benchmarks and a social sustainability roadmap for decision-makers to improve SP. This study undertook a novel and participatory approach to social life cycle assessment (S-LCA). Respondents from both within and at-arms-length to the beef value chain were engaged to identify SP practices and perceptions and develop the life cycle inventory. The goal and scope, inventory, assessment, and interpretation were conducted in a three-phased approach: (i) scoping; (ii) practice-based assessment; and (iii) deep-dive assessment. Data collected through mixed methods, including Q method, interviews, surveys, and literature review, were assessed using a type I (reference scale) approach and interpreted through critical interpretive synthesis. Organizational SP was explored at BFBs and BSOs, considering the following stakeholders: value chain actors, farmers, employees, and cattle. Outcomes from the applied approach explore the interrelations between organizations and stakeholders along the value chain. Impact categories concerning labour management, people’s health and safety, and animal care were prioritized for deep-dive assessment. Subcategories included novel topics, like recruitment and retention and access to mental health resources. A range of potential benefits and areas for improving SP were identified at BFBs and BSOs. Potential risks from SP were identified for stakeholders from working hours, communication and dispute resolution, animal transport, and personal protective equipment practice areas. Results informed strategic activities for the National Beef Sustainability Strategy. This study contributes to the social sustainability discourse in livestock systems by demonstrating a qualitative approach to S-LCA that can be replicated by practitioners to explore valid and locally specific social dimensions of sustainability. Practitioners may consider the approach and results in future studies to better capture and manage the complex and dynamic nature of livestock systems, leading to more effective social sustainability decisions that incorporate diverse stakeholder perspectives.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".