Project Thinking as the Basis of Design Creativity: Content and Features of Its Development among Bachelor-Designers
Bibliographic record
Abstract
In current conditions, without the ability to think outside the box, without the formed artistic-design competency that identifies the ability to create a design idea based on conceptual and creative approaches to solve the design problem, a specialist cannot be in demand, competitive in the professional field. The content of education in the field of professional training of designers should provide graduates with opportunities for freely promotion along with various professional ways (Koveshnikova & Koveshnikova, 2012). The urgent task of the modern professional training of designers in higher education has been and remains the development of creative project thinking of future specialists. It can be developed in various ways, one of which is the use in the training of bachelors-designers, the integration of photographics and academic disciplines, the aim of which is to teach students the basics of graphic design. The article reveals the essence of project thinking and the role of Photographics in developing project thinking among designers in the process of their art-project activities. Particular attention is focused on the specifics of project thinking as a priority during the training process in the field of design. Photographics is interpreted in this study as a modern form of visual art, capable of developing components of project thinking in integration with graphic design.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".