Labelling Information Provided in the E-Grocery (Online) Retail Environment: A Scoping Review Protocol
Bibliographic record
Abstract
The COVID-19 pandemic has led to increases in the number of people purchasing grocery products from online retailers. In response to a changing market, brick-and-mortar stores have shifted to compete with those retailers who had a pre-established presence in the online retail environment. When shopping for groceries online, customers do not see the physical product before their purchase, so, they rely on the information that is provided to them on the product page. Important information about the food product's ingredients, nutrition, and source which is found on the physical products is often excluded from product pages. This exclusion may prevent consumers from making informed healthy decisions about the food products they are purchasing. Within Canada, food labelling falls under the Food and Drug Regulations and the Safe Food for Canadians Regulations. Under the regulations, foods and beverages sold within Canada must provide accurate and consistent labelling information about the product. Despite these requirements, the food labelling regulations do not outline requirements to sell food products in the online space. This is significant because it can prevent customers who purchase groceries online from selecting foods based on nutrition, ingredient, allergen or manufacturing information. This scoping review protocol outlines the strategies to collect information on online food labelling regulation in countries that are economically similar to Canada (the Group of 12 [G12] countries) and countries in the European Union who currently have these regulations in place. This scoping review will outline online food labelling availability in the online retail environment and regulation in each of the included countries. Additionally, this scoping review will map the characteristics of research on food labelling in the online retail environment.
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 | 0.087 | 0.122 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.086 | 0.018 |
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".