Bibliographic record
Abstract
There is growing evidence that a significant share of global food is thrown away, with concomitant detrimental repercussions for sustainability. Reducing food waste is a key sustainability challenge for the foodservice industry. Despite the significance of this issue to the global foodservice industry, the link between innovation practices and food waste management has received limited attention in the academic literature. This paper uses innovation management and social constructionism to investigate the interrelationships of foodservice provisions and innovations in waste management. It is based on the evaluation of food waste solutions and innovations that combine strategic dimensions of waste management with practice-driven initiatives, including incremental (processes and technologies) and radical innovations. The paper presents a range of waste management initiatives, showing that their implementation in the foodservice sector varies depending on management’s beliefs, knowledge, goals, and actions. The concepts discussed here could help practitioners to become more aware of the factors that drive the adoption of food waste innovations. The prevalence of food waste has been a subject of interest and discussion in recent years and researches is being done to find effective ways to curb it. It has been identified as a primary issue in the sustainability of food production and consumption, in addition to the sustainability of food supply chains. Food waste can be divided into avoidable and unavoidable waste. Avoidable waste includes edible food and spoiled/damaged edible food, while unavoidable waste consists of inedible food like bones, fruit peels, and eggshells among others. Research shows that in Finland, 5% of purchased food is wasted in households, and an average person wasted about 20-30 kg of food in a year. The average total amount of food wasted in households yearly is about 120-160 million kilograms. Household wastage could be intentional or not. Many of the food wastage in households could be a result of forgetfulness or negligence for the food expiry date. In countries like Finland with the high cost of living, consumers are inclined to buy food nearing its expiry date due to the discount shop sellers attach periodically.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.011 |
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".