Bibliographic record
Abstract
The New York Times Magazine recently featured an article provocatively titled "The Creative Apocalypse That Wasn't" (Johnson, 2015) in which the author surveyed statistical data from the U.S. Labor Department and the U.S. Economic Census to arrive at a somewhat upbeat assessment of the economic well-being of the creative economy.The article suggests that the recording industry's recent shortfalls in album sales is negated in part by new revenue streams from live performance; Hollywood's abandonment of mid-budget films in favor of formulaic blockbusters and endless parades of sequels is made more palatable by the release of more eclectic films by independent production companies; the emergence of blockbuster economics in e-book publishing is made more palatable by niche markets supported by indie bookstores.In other words, our collective anxieties about the economic impact of recent technological developments in the creative economy are unwarranted because "…there are now more ways to buy creative work, thanks to the proliferation of content-delivery platforms" (para.28).Likewise, "...just as there are more avenues for consumers to pay for creative work, there are more ways to be compensated for making that work" (para.29).The author concludes that the recent restructuring of production, distribution, and consumption has not significantly impacted the prospects of earning a livelihood within the creative industries.Simply put, consumers can now choose from a larger selection of creative works than ever before and that means more people are getting paid to do what they love.In general terms the creative industry is analogous to the sector known as the Information and Cultural Industries as defined by the North American Industry Classification System (NAICS).Developed by the U.S. Office of Management and Budget (OMB), NAICS is a hierarchical classification system of industry sectors, subsectors, and groups.NAICS is also the departmental standard for Statistics Canada and Industry Canada.According to Industry Canada (n.d.),
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".