Bibliographic record
Abstract
Social networking is a causative factor for obesity in several ways: 1) Obesity is frowned upon socially. Social rejection and isolation produces sadness and depression, which elicits "comfort eating", typically involving highly pleasurable food. The pleasure and distraction of eating eases emotional pain and depression -a form of self-medication. Some obese people even proclaim, "food is my best friend," thus replacing their social networking. This may become a vicious cycle with more weight gain, more rejection, more comfort eating, even lower self-esteem and more isolation, and so on. 2) The brain is averse to emotional pain and undergoes changes to keep the comfort eating behavior going. The person may thus develop a dependence on the pleasure of comforting foods -an actual addiction -which neuromaging data is now confirming. 3 ) Social rejection and isolation of the obese person lowers self-esteem, which tends to result in the person not caring about gaining more weight, another vicious cycle. 4) Fear of social criticism in obese people induces shame and embarrassment. They may thus keep weight issues a complete secret and may be too embarrassed to seek help, a third vicious cycle, plus additional comfort eating to cope with the shame. Social networking can likewise combat obesity in several ways: 1) Social support reverses isolation, sadness, and comfort eating; support groups have been long known to enhance weight loss efforts, e.g. Overeaters Anonymous (OA). 2) Social networking enhances self-esteem, especially when the obese person experiences success at losing weight and becomes a mentor to those just starting out. Being a mentor is a win-win situation, as this also reinforces the mentor's life changes. The goal of food addiction support groups, like OA, is to become a mentor. 3) Social networking offers accountability pledging self-actions to a group takes advantage of peer pressure. 4) A weight loss "buddy" is very desirable to obese individuals, and offers accountability, mutual problem solving and resisting cravings and binges. 5) Online social networking is a new tool, consisting of bulletin boards, chat rooms, success stories, weight loss buddies, and tips. Online social networking can offer the advantage of anonymity, which avoids shame and embarrassment. Non-anonymous online social networking, e.g. Facebook, is less useful. Facebook, as the name implies, is based on face photos , to which obese people are averse, and 93% of Facebook "friends" know each other in real life. Social networks can facilitate breaking the addiction (problem food) cause of obesity. Group support helps the obese person tolerate withdrawal from problem foods and adds motivation to keep going. Re-addiction is prevented by socially learning to cope with life without turning to food.Online social networking will be demonstrated via a website used by thousands of overweight kids. A smartphone app obesity intervention will also be demonstrated, based on the addiction model, which uses extensive online social networking (buddies, groups, and mentors).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.497 | 0.370 |
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".