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Record W7061895210

Serving the Right Plate: Spatial Biases in Food Plating Aesthetics

2022· dissertation· en· W7061895210 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)PopularityPlating (geology)PhotographyOrder (exchange)Laterality
DOInot available

Abstract

fetched live from OpenAlex

With the growing popularity of social media, the aesthetics of food plating is getting more and more exposure. Optimizing the aesthetics of plating and understanding where certain elements of the dish should be placed on a plate or within a bento box or bowl will aid researchers in learning what side of dishware plating creates the most appetizing appearance to the average diner. The goal is to understand the ideal layout of a dish in order to improve a chef’s plating techniques along with optimal advertising photography for a world focused on visual stimuli. Previous literature led us to predict that there would be a leftward bias of plating the largest, most caloric heavy component of the meal on the dish. Similar to Western text (both reading and writing) moving from left-to-right, we suspect diners prefer food being placed in a similar directionality. Art is commonly studied in conjunction with laterality experiments, where participants frequently demonstrate a leftward bias. Japanese bentos are normally rectangular in shape, akin to a portrait or painting. When scanning a photograph, people generally scan from left-to-right similar to when reading text. Research in laterality suggests that native reading direction can highly influence a person’s opinion on aesthetics. This thesis outlines studies of food plating aesthetics whereby Canadian participants were presented photographs of professionally plated dishes and their mirror image together to elicit and record preferences. Image pairs were presented on top of each other in random orders, and decisions between leftward and rightward biased choices were collected and analysed. The majority of participants had a native reading direction of left-to-right. Interpretations of the results suggested what the ideal way to plate food is. For food presented on plates, no significant placement bias was present overall. However, for bento boxes and poke bowls, participants preferred dishes where the majority of the food was plated on the left side of the dishware, resulting in a statistically significant leftward bias. Additionally, a particular favourable trend was consistently seen, where participants almost always preferred a placement of long foods to start at the bottom left corner and ascend towards the top right in a diagonal pattern. Research in laterality and aesthetics suggests that people have a particular penchant towards either specifically the left or right side for many different habits (such as preparing the right hand for a handshake) or appearances (such as preferring artwork that is illuminated from the left). By applying these studied concepts to the art of plating, techniques can be taught to create ideal presentations. Learning how we can optimize plating aesthetics can benefit people in a variety of vocations. Mainly chefs, cooks, photographers, social media users, advertisers, and marketers could use this conclusion of a leftward bias preference to their advantage.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.008
GPT teacher head0.190
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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