“Chess for overall development” software in the frame of Reflection and Activity
Bibliographic record
Abstract
The paper discusses “Chess for overall development” project, which is based on Reflection and Activity Approach in helping overcome learning diÿculties. The project has been running for over 12 years now, putting theory of the approach into practice in several cities throughout the Russian Federation. One of the key elements in this project is developing the ability to think in mind using chess problems and sequential progress through material where solving of problems becoming more idea-based and less action-based using the notion of the stage-by-stage formation of mental actions. Unlike other methods teachers use to teach chess, the Chess for Overall Development project views chess primarily as a psychological instrument for helping develop the ability to think in mind. The basis for the ideas of the project is L. S. Vygotsky’s cultural-historical psychology. Vygotsky’s notions on the development of the human psyche are implemented and partially expanded as part of the project. In recent years the software “Chess for overall development” was made designed with full compatibility with the principles of the reflection and activity approach and implementing the notion of the ability to think in mind development and sequential transition from material to ideal plane of mental actions. We present detailed description of the software features as well as its approbation results collected in schools, universities and hospitals.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".