Embracing imperfection in free play
Bibliographic record
Abstract
Embracing Imperfection in Free Play explores the interconnections between childhood imagination, attachment to landscapes, free play activities, play sculptures, and abstraction in sculpture. Inspired by the snow-covered landscape of Manitoba, this artistic project symbolizes liberation and self-discovery, challenging societal norms and embracing imperfections. Through two sculptural projects and a stop motion animation, the project examines the influence of childhood homes, society, and landscapes on individual perceptions, fostering a transformative and interactive experience for viewers. Additionally, the project delves into the role of playful activities in strengthening parent-child relationships and promoting self-discovery and acceptance in society. The project also focuses on exploring playground design and free play in contemporary art and architecture, highlighting the importance of creating engaging, inclusive play spaces. The research delves into the works of influential artists in this field to gain deeper insights into the concept of free play and its impact on artistic practices. This text also discusses how I cope with perfectionism through sculptural works, seeking belonging and protection from negative emotions while fostering an imaginary connection to space and community, symbolizing a journey of introspection. Moreover, the performative aspect of viewer engagement serves as a potential framework for incorporating performance into my artistic practice, further enriching the artistic encounter. Overall, the project presents a comprehensive exploration of abstraction in sculpture, playground design, landscape, and the transformative power of art, inviting viewers to interact with the artworks and embrace the beauty of imperfections.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".