Inuit-defined determinants of food security in academic research focusing on Inuit Nunangat and Alaska: A scoping review protocol
Bibliographic record
Abstract
Background Academic research on food security in Inuit Nunangat and Alaska frequently adopts the Food and Agriculture Organization of the United Nations' working definition of food security and Western conceptualisations of what it means to be ‘food secure’. However, in 2014, the Alaskan branch of the Inuit Circumpolar Council (ICC) stated that academic and intergovernmental definitions and understandings ‘are important, but not what we are talking about when we say food security’. The organisation subsequently developed its own conceptualisation and definition: the Alaskan Inuit Food Security Conceptual Framework (AIFSCF), which in 2020 received informal assent by ICC-Canada. Aim This protocol establishes a review strategy to examine how well academic research reflects Inuit conceptualisations and understandings of food security, as outlined in the AIFSCF. Methods Review structure and reporting will be completed according to adapted RepOrting standards for Systematic Evidence Syntheses (ROSES) guidelines. A comprehensive search strategy will be used to locate peer-reviewed research from Medline, Scopus, Web of Science and the Arctic and Antarctic Regions (EBSCO) databases. Dual reviewer screening will take place at the abstract, title, and full-text stages. Different study methodologies (qualitative, quantitative, and mixed methods) will be included for review, on the proviso that articles identify drivers of food security. An a priori coding framework will be applied by a single reviewer to extract data on publication characteristics, methods and article aims. Deductive thematic content analysis will then identify the frequency and precedence afforded within literature to the drivers and dimensions of food security identified by the AIFSCF.
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 | 0.140 | 0.144 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.054 | 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".