Investigating the impact of eating norms and collective autonomy support vs. collective control on unhealthy eating and its internalization
Bibliographic record
Abstract
Our eating behaviors are highly influenced by those of individuals surrounding us and the groups we belong to. The first goal of this experiment was to determine how social norms that encourage (pro-) vs. discourage (anti-) unhealthy eating influence people's intentions and motivations to eat unhealthily. Since these norms can be conveyed by one's group in a manner that either promotes group members' autonomy (i.e., collective autonomy support), or pressures them into eating certain foods (i.e., collective control), the experiment also tests which of these types of messages promotes the highest conformity to group norms. Hence, the second goal of this experiment was to investigate this synergetic effect of pro- vs anti-unhealthy eating norms and of collective autonomy support vs. collective control on participants' unhealthy eating intentions and their motivations for unhealthy eating. An experimental study (N = 341) using a 2 (eating norm: pro-unhealthy eating norm vs. anti-unhealthy eating norm) x 3 (type of group support: collective autonomy support vs. collective control vs. no support) design was conducted. Results showed that pro-unhealthy eating norms increased participants' intentions to eat salty and fatty food, but also their amotivation (i.e., lack of motivation) for unhealthy eating relative to anti-unhealthy eating norms. In addition, when pro-unhealthy eating was encouraged in a controlling (vs. in an autonomy supportive) manner, participants reported higher intentions to eat tofu tacos. Finally, when pro-unhealthy eating was promoted by supporting group members' autonomy, participants reported higher integrated regulation, i.e., a highly internalized motivation, for unhealthy eating. These results demonstrate that eating norms do not impact all types of unhealthy food consumption in the same manner, and that collective control may be motivating in uncertain contexts; furthermore, when individuals' autonomy is supported and promoted by other group members, they are more susceptible to integrate unhealthy eating in their life.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".