Impacts of traditional food consumption advisories: Compliance, changes in diet and loss of confidence in traditional foods
Bibliographic record
Abstract
BACKGROUND: Food consumption advisories are often posted when industrial activities are expected to affect the quality and availability of traditional foods used by First Nations. We were recently involved in a project and asked to summarize details regarding the impacts of traditional food consumption advisories with respect to compliance, broader changes in diet and loss of confidence in traditional foods by people. METHODS: Our review was not conducted as a formal systematic comprehensive review; rather, we focused on primary and grey literature presenting academic, health practitioner and First Nations viewpoints on the topic available from literature databases (i.e., PubMed, Web of Knowledge (SM)) as well as the internet search engine Google. Some information came from personal communications. RESULTS: Our overview suggests that when communicated effectively and clearly, and when community members are involved in the process, consumption advisories can result in a decrease in contaminant load in people. On the other hand, consumption advisories can lead to cultural loss and have been linked to a certain amount of social, psychological, nutritional, economic and lifestyle disruption. In some cases, communities have decided to ignore consumption advisories opting to continue with traditional lifestyles believing that the benefits of doing so outweigh the risk of following advisories. CONCLUSIONS: We identified that there are both positive and negative aspects to the issuance of traditional food consumption advisories. A number of variables need to be recognized during the development and implementation of advisories in order to ensure a balance between human health, maintenance of cultures and industrial activity.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".