Bibliographic record
Abstract
Whereas critical dietetics is still primarily centered on the Anglo-American countries, the movement has recently come to develop in the Nordic countries as well. On the 25th of August 2017, the first conference for critical dietetics in Scandinavia was held at Uppsala University, Sweden, and, following this, a network has been established: the Nordic Network for Critical Dietetics (NNCD). This paper is yet another step in this internationalization of critical dietetics. We discuss why we think that the Nordic countries have great potential to develop critical dietetics into an influential part of the future development of this promising international movement. We then explore three cases from a Nordic context that we see as touching upon, or being relevant for, critical dietetics. These are thematized as “Challenges to individualism and cognitivism in behavioral change policies,” “Working against stigmatization of obesity and ‘bad’ lifestyles,” and “Trust and recognition: challenges to the professions.” Each case is illustrated by a few examples of Nordic research and current public debates and campaigns from Sweden. The main argument is that the Nordic region already has a soil in which seeds of critical dietetics have been sown; it is time for them to grow.
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 | 0.039 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.030 | 0.059 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".