Waging Culture : Interrogating the Visual Artist Labour Force
Bibliographic record
Abstract
"Over the past ten years, Michael Maranda of the Art Gallery of York University has been conducting the Waging Culture survey of the socio-economic status of professional visual artists in Canada. The data collected from this survey has brought forward a picture of a sector where the majority of the primary producers are essentially working for free, supplying the raw material with which the entire contemporary art sector functions. For those familiar with the sector, that day-jobs pay the rent for artists is not that surprising, but a solid, data-driven illustration of the extent of the need for such side-hustles is important to truly understand the intertwined mechanics of the sector. \n \nThis book, drawing primarily on the 2017 survey, includes a speculative look at some of the drivers of the artist labour force. In particular, we contrast a sales-driven winner-take-all model that exacerbates income inequality with a much more equitable grant-driven work-preference model. Accompanying this analysis, we also describe some of the perverse correlations of educational achievement to financial success in the field, with “profitable” artistic practices being negatively correlated to educational achievements." -- Publisher's website.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".