Bibliographic record
Abstract
The purpose of this research is to examine the best practices to adopt modern API technologies, web servers, and frameworks to create tools that run on most design/creative industry software. This system is mainly for time, task, and project management on the remotely executed projects, which individual visual designers and teams can use. A prototype was created to support this research and demonstrate and examine the further possibilities of this system. The prototype is in the form of an extension for Adobe XD. Moreover, it analyzes the time and activity of the end-users. The prototype was created to study the effectiveness of a non-linear time/task management ecosystem for remote workers, primarily users in the creative community who work on visual development software. It can also be utilized to study and validate the efficacy of using an interconnected supervision agent for teams. Consequently, it provided a platform to determine how a team can manage the time and tasks without being sidetracked using a third-party service and focus on the work at hand by staying within the same workspace where the project is being executed. Ultimately, a PWA (progressive web application) was created to share usage analytics and tasks publicly on the web. Additionally, an API was designed and built to store the data and make it available for further production. The API, built on Node.js, stores data in a NoSQL database and makes it available to other applications and platforms. The relationship between these parts illustrated a productive way for experts to stay connected with the project managers who are not necessarily skilled in using the design tool. In addition, the difference in how the user interface was designed for each section provided means to examine the benefit of providing multiple user-fronts while keeping the data consistent among the designers/developers and the managers/clients.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".