Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mobile applications (commonly known as ‘apps’) are highly popular forms of software, with hundreds of billions of downloads globally each year. The ways in which the affordances of apps are portrayed on the app store platforms are crucial in sparking consumers’ initial interest. This article presents findings from the ‘Mapping the Food App Landscape’ study. The following two sources of online material were used in this study: (1) descriptions of food-related apps available in the Google Play store; and (2) the lists of the top-most installed free Android apps presented in the App Annie app analytics platform for Australia, Canada, the United Kingdom and the United States. The analytical approach is distinctive in bringing together the key feminist new materialism concepts of affective forces, relational connections and agential capacities with that of the promissory narrative. The study’s findings show that inapp publishers’ efforts to entice users, the Google Play app descriptions presented food apps as solutions to or escapes from the stresses and difficulties of everyday life. These app descriptions promised to generate excitement, fun and pleasure (games apps); configure and support convenient food supply and preparation arrangements (food ordering and delivery, meal planning and recipe apps); offer reassurance and better control over the body and encourage greater embodied self-awareness, health and wellbeing (food-tracking and nutrition apps); and contribute to creative and novel experiments in cooking (recipe apps). These findings map the landscape of food apps and the sociocultural contexts in which they are being created, published and adopted.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it