Clam research in Nunavut: A scoping review of the literature
Bibliographic record
Abstract
Clams are an important country food with cultural, environmental, and health significance for Inuit communities in Nunavut. We analyzed the extent, range, and nature of published research on clams in Nunavut, Canada. We used a systematic and transparent scoping review methodology by applying a search string across three databases to identify potentially relevant articles. Two independent reviewers screened the titles and abstracts (phase 1), followed by article full texts (phase 2), using inclusion and exclusion criteria. Data were extracted from 24 included articles and descriptively analyzed. We also conducted thematic analysis to identify overarching themes, ideas, and gaps. The most frequent topic of research was using clams to understand ecological histories ( n = 10/25; 40 % ), followed by the biology of clams ( n = 7/25; 28 % ), environmental indicators ( n = 6/25; 20 % ), and foodborne illnesses ( n = 2/25; 8 % ). We did not identify any articles that investigated the nutritional value of clams, food security, or Indigenous knowledges. Out of all included articles, just over one-quarter described Inuit involvement in the research ( n = 7/25; 28 % ) . Our review highlights and documents how clam research has predominantly focused on natural and environmental sciences in Nunavut. Published research that explores health and social dimensions of clams in Nunavut has so far been limited. Given that clams are not only an ecologically important species but also hold health and cultural significance for communities in Nunavut, further research to capture a diversity of topics – as well as the intersection among topics – could support food-related programming, policies, and decisions intended to foster Inuit wellbeing.
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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 | 0.027 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.031 | 0.033 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".