#INSERTDIETHERE: Tracing the Techno-Linguistic Associations of Dietary Hashtags on Instagram
Bibliographic record
Abstract
The use of hashtags on social media platforms allows users to navigate vast repositories of information with ease -hashtags technologically mediate as they both enable and shape user experience.This research focuses on the photo-sharing platform of Instagram and the phenomenon of dietary hashtags (DH: singular; DHs: plural) -for example, #healthy, #glutenfree, #vegan, and #whole30.The circulation of these hashtags is analyzed to explore if and how they can influence users' understanding of diets and eating practices.The study uses Actor-Network Theory to describe the performances and impacts of DHs within a technologically mediated space of social media.Through experimenting with the walkthrough method and an Instagram narrative model, the thesis observes discursive associations in dietaryrelated Instagram content.Sample case studies look at the ways in which Instagram has enlarged the sphere of possible associations, which consequently alter food and diet related acts.DH discourses and the ways in which they impact dietary visibility, proliferate dietary belief systems, intentional DH performances (user subjectivities), and dietary-based social/communal affiliation (intersubjective discourse) are first considered separately.The results indicate that Instagram supplements and even displaces food-related acts.Moreover, Instagram creates a virtual environment in which visual and linguistic dietary discourse is performed; thus altering how users learn about food practices, as well as enabling the making of dietary-based associations.The thesis concludes by linking these elements and details how these components assemble in the making of a dietary techno-cultural space.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".