Bibliographic record
Abstract
Tipping Point explores the accelerating impact of climate change through the metaphor of receding glaciers, using textiles, sound and material processes to reflect on environmental fragility and transformation. At the heart of this work is a reimagining of structure and support: where traditionally, pins hold fabric layers together, here it is the ice that holds the pins, forming a fragile, mesh-like configuration that slowly disintegrates in an inversion that speaks to the instability of our natural systems. Just as glaciers carry and deposit rock debris over time, layers of ice and embedded pins accumulate in this work, evoking the complexity of a double cloth, woven in two distinct layers that interlace through a shifting warp and weft structure. This textile reference serves as both a visual and conceptual framework for understanding the complex, interwoven nature of ecological change. Accompanying the installation is a soundscape composed of field recordings from Norway, interwoven with the subtle, intimate sounds of pins and ice. These sonic elements are layered in a manner akin to textile construction – threaded, overlapped, and merged – to create an immersive auditory environment that evokes the sensory experience of climate disruption. By drawing connections between cloth, ice, and sound, Tipping Point considers the delicate thresholds we now inhabit – where structural bonds weaken, where memory is held in material, and where the slow unravelling of our climate is both felt and heard.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.418 | 0.143 |
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".