Benchmarking and understanding London’s Cultural and Creative Industries
Bibliographic record
Abstract
This paper describes the Greater London Authority’s evidence base for its work on the creative and cultural industries. Its main purpose is to show that th9is evidence base is viable, robust, and useful. The second and most important purpose is to encourage others in city management to invest in such evidence bases, and to compile them on a comparable basis. It will be some while before this is done by international agencies, and that national agencies are only at the start of a long journey in recognising the importance of city data. Hence, I argue in this paper, a responsibility devolves onto the cities themselves. This paper is about those responsibilities. The paper was originally presented to the Conference Board of Canada at its March 2008 international conference on the creative industries, and, along with the conference proceedings, can be obtained from the conference board via www.e-library.ca. or www.conferenceboard.ca
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".