Bibliographic record
Abstract
E mergence" is defined as the process of an idea, person or entity issu- ing forth from "concealment, obscurity or confinement." 1 Industrial innovation, apparatus prototypes made with emerging technologies and objectoriented performance were trending in the circus world at the beginning of 2019.A growing number of inventions, re-inventions and hybrid props inspired physical performance vocabularies and discussions about a new materialism in the circus arts.Then, the global pandemic hit.Most research and development projects, new show creations and live performances either slowed or stopped completely.From the Global North to the Global South, circus companies, organizations, schools, universities, researchers, festivals and artists had to survive emergency conditions during the economic, social and health impacts of COVID-19.These impacts are still felt today, and the struggle continues.Some community members adapted, survived, pivoted, and even thrived.Others moved to different industries.The pain, isolation and inventiveness of this historical moment influenced the circus arts in a number of ways.The concurrent Black Lives Matter movement and other social justice issues that were illuminated during this time and its political unrest sparked conversations, demonstrations and change throughout the circus industry.Other historical events have similarly influenced both circuses and the societies in which they exist.In the Arts section of this issue, "Des circassiens périphériques aux prises avec la globalisation : Trajectoires transnationales et controverses esthétiques" (Peripheral Circus Artists Struggling with Globalization: Transnational Trajectories and Aesthetic Controversies) discusses the emergence of circus in post-dictatorial Chile.Through ethnographic case studies of Chilean artists who received professional training in Europe, Aurore Dupuy discusses the materialization of new artistic identities as well as the institutionalization of circus creating hierarchies
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.184 | 0.082 |
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".