Bibliographic record
Abstract
Seixas, Ana. Tinybop. Me: A Kid’s Diary. 2016. Apple App Store, https://itunes.apple.com/app/apple-store/id1126531257?mt=8. Ages 3-7 (depending on parent assistance)Cost: $2.99 This app allows young children to create a digital diary filled with their own writings, photos, audio recordings, and drawings. The child creates an avatar from a varied array of options for skin colour, hair colour and style, facial features, and accessories. The app then encourages the child to respond to prompts, such as, “A song about me would be titled…,” “This is an interesting fact about my family,” and, “If I were an animal, I would look like this.” Some questions require a textual response, while others ask the child to draw, record, or take a snapshot of their response to the prompt, thereby taking advantage of the affordances offered by a tablet or phone. Other activities include the option to create a family tree, to create avatars of the child’s friends, and to answer all kinds of questions about the people in the child’s life. A child can draw, record, and photograph daily activities, such as their life at school. Children can use the app to explore their own ideas, experiences, and feelings through both serious and silly questions. A Kid’s Diary takes a simple process and makes it even more accessible to quite young children. Ana Seixas’ illustrations use eye-popping colours, with good use of contrast and negative space to make clicking easy. The language of the questions is simple and displayed in a large font. Younger children should be able to use this app with the help of caregivers reading the text for the children’s answers. Caregivers should know that the company foregrounds their privacy policy on the developer site, noting that the app does not collect information about the users through the application itself. It is highly recommended as a fun way for children and their caregivers to learn more about themselves and the world they observe around them. Highly recommended: 4 out of 4 starsReviewer: Allison Sivak Allison Sivak is the Public Services Librarian at the University of Alberta Libraries. She is currently pursuing her PhD in Library and Information Studies and Elementary Education, focusing on how the aesthetics of information design influence young people’s trust in the credibility of information content.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.203 | 0.139 |
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".