From <em>Taste of Home to Bullipedia</em>: collaboration, motivations and trust
Bibliographic record
Abstract
Bullipedia, the online gastronomic encyclopedia, is an idea yet to be developed. In this work, we analyze a community formed around a food magazine, Taste of Home (ToH), and extract some good practices to incorporate them to the future Bullipedia. ToH is a recipe exchange magazine specialized in Midwestern cuisine. Its recipes are reliable as they are from home cooks and tested by ToH's professional chefs. This model of user created content (curated by culinary experts) generates trust and a strong sense of community among like-minded people who share food preparation tips and anecdotes. In this paper, we present a study on the interaction network of ToH community in order to understand major motivators for its members to contribute. In our approach, we analyzed the ToH community through its social network on Facebook. Firstly, we selected a subset of users that posted on ToH's timeline. Then, we created connections between two users when one of them commented on the other's post. Finally, we extracted the topology of the social network and identified the main nodes. Our key finding is that this community is very poorly structured and hierarchical, and that ToH is placed in a central position, which gives it full control of all flow of information in the network. The fact above is important for a magazine whose business model is based on advertising, as it ensures its users always have to visit its own sources of information. We propose to adopt such demonstrated successful model to the Bullipedia. Bullipedia, l'encyclopédie gastronomique en ligne, est une idée qui reste encore à développer. Dans cet article, nous avons analysé une collectivité qui s'est formée autour d'une revue culinaire, Taste of Home (ToH), et en avons extrait quelques bonnes pratiques afin de les incorporer à la future Bullipedia. ToH est une revue d'échange de recettes spécialisée dans la cuisine du Midwest. Ses recettes sont dignes de confiance car elles proviennent de cuisiniers et cuisinières maison et ont été testées par les chefs professionnels de ToH. Ce modèle de contenu créé par l'utilisateur (et organisé par des experts culinaires) inspire la confiance ainsi qu'un solide sentiment de communauté parmi des personnes qui ont les mêmes aspirations et qui partagent des conseils et des anecdotes sur la préparation des repas. Dans ce document, nous présentons une étude sur le réseau d'interaction de la collectivité ToH afin de comprendre les principaux motivateurs de contribution de ses membres. Dans notre approche, nous avons analysé la collectivité ToH par l'entremise de son réseau social dans Facebook. D'abord, nous avons choisi un sous-ensemble d'utilisateurs qui ont affiché sur la page de ToH. Ensuite, nous avons créé des liaisons entre deux utilisateurs lorsque l'un a commenté l'affichage de l'autre. Enfin, nous avons extrait la topologie du réseau social et avons identifié les principaux nodules. Selon nos principales constatations, cette collectivité manque totalement de structure et est très hiérarchisée, et ToH se trouve dans une position centrale, ce qui lui donne le plein contrôle du flux des informations du réseau. Le fait ci-dessus est important pour une revue dont le modèle d'entreprise est fondé sur la publicité, car cela garantit que ses utilisateurs doivent toujours visiter ses propres sources d'information. Nous proposons d'adopter un tel modèle de réussite éprouvé pour Bullipedia.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".