Food motivation: content analysis of <i>Châtelaine</i> women's magazine
Bibliographic record
Abstract
Purpose The purpose of this study is to explore the continuity and/or rupture in food motivations as revealed from documents issued from Châtelaine in 1960‐1961, 1970, 1980 and 1990. Design/methodology/approach A historical content analysis was used to explore the food motivations in Châtelaine. A total of 51 issues were read and all documents referring to food and nutrition were photocopied except for advertisements. Variables associated with each document were grouped into four themes: health and diseases, food and nutrition, body and society. Descriptive statistics were conducted using SPSS (version 10.1, 1999). Findings Overall, the content analysis has indicated that food and nutrition is the dominant motivation (n = 430) followed by health and disease (n = 292), society (n = 71) and body concerns (n = 70). Each decade was associated with major sub‐themes. In brief, 1960 was the year of food, family and tradition; 1970 was the transition year; 1980 was the year of knowledge, culpability and environment; 1990 was the nutritional education year. Research limitations/implications Study done using the first year of each decade and in a single magazine cannot be generalized. Moreover, these results are specific to a French‐Canadian context. Further research on other media could provide more insight into some of the relationships explored in this study. Originality/value The findings suggest that the dominant motivations are projecting in a different way from one year to another. These results support the need to take into consideration the role of media in shaping women's food preferences and the evolution of these motivations over time.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".