Bibliographic record
Abstract
This chapter explores the unintended side effects of an art performance called Tisser du lien (To Weave Links) that embodied a common French saying by shaping a living sculpture made of people woven together. Through the prisms of anthropology, psychoanalysis, mythology, and sociology, a different kind of understanding emerges of what was initially an act of rebellion. Tisser du lien questions the role of the artist as a medium or transformative agent who weaves relationships among participants in a performance. This process can be related to how everyday means are used to escape from the controlling strategies of institutions, invoking the metis or wisdom of many heroines of Antique Greece mythology and the tactics of Michel de Certeau. Part of such strategies is noting the difference between verbalized knowledge as a tool often wielded by the elite and procedural knowledge as a means of reclaiming the power of making and doing something. Weaving and fiber arts materialize deep human psychic needs, and the performance process can be seen as a way to shape participants as subjects. Subsequently, these interactions open up in a rhizomatic manner wherein the tactility associated with the symbolic creates psychic effects on the individual self and on the assembly.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".