Bibliographic record
Abstract
Zanotto, Lucas. LOOPIMAL. Music by Ulrich Troyer. Software by Niels Hoffmann. Vers. 1.1.0, YATATOY, 4 Feb 2016. Apple App Store. https://itunes.apple.com/app/loopimal/id964743113The team behind YAYATOY have created their third delightfully simple app. LOOPIMAL is a game of musical choreography which introduces computer sequencing to children.The game begins with one of the six of the adorable animals nodding along to some background music. Beneath the animal, differently coloured shapes are waiting to be dragged and dropped onto the sequencing bar. Each shape corresponds to a different sound and dance movement. Fortunately, the music is composed in C major so all of the endless combinations sound great together. This makes the game a safe space for creation without the risk of failure.Like their previous apps, it has a very intuitive non-lingual interface that would be easy for a very young child to navigate. There is only one simple visual menu, and it is easily located as a plus sign in the top right corner of the screen. Clicking it allows the user to choose a split screen sequence of two or four animals, which layers each animal’s sequence of music.The charming animal animations combine perfectly with the modern music to teach children how to connect symbols to actions, which is a core element of coding. The user can continually alter the loop by changing the sequence of the shapes, or they can simply swipe in either direction to try a different animal with a different sound.This app is an extremely original way to introduce basic coding concepts to very young children. As computers become omnipresent in our daily lives, it is crucial for children to be introduced to concepts like this early. Yet the app does not feel boringly didactic. It is extremely fun to tinker with, even as an adult. Indeed, it is difficult to not smile at an octopus using its legs as bass guitar strings.Highly recommended: 4 stars out of 4 Reviewer: Kyla LeeSuggested age range: 4-8Price: $5.49Kyla Lee is a first year student in the Library and Information Studies program at the University of Alberta, and a Library Assistant at EPL. She is very interested in helping youth develop digital literacy skills from a young age, and incorporating creative apps into programming.
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.622 | 0.607 |
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".