Bibliographic record
Abstract
With the vast number of distractions that exist in our lives today, it is very hard to stay focused on our goals and accomplish our tasks. Smartphone apps have the potential to be very constructive, but unfortunately sometimes have a destructive effect on our lives. Smartphone- based goal and activity tracker applications can provide users with data to support self-reflection, allowing the user to be more aware of their behaviors, and how those behaviors map to explicit goals and values around attention, focus, etc. The use of Gamification and data visualization is becoming more and more popular in lifestyle and social applications, and it is proven to be useful in increasing the users’ productivity. There are many goal setting apps available on different platforms at the moment, other than a few, they mostly lack engagement with the users and, in other words, they do not provide any sort of tangible reward for the accomplishment of tasks. Planetarium is a project that brings gamification to users’ short term and long term goals as well as the opportunity to reflect on personal well being and productivity. This major research project explores ways to bring productivity to the user’s day to day lives through gamification and visual representation of the users’ goals and daily behavior.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.262 | 0.088 |
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".