Bibliographic record
Abstract
For over a decade, scholars have considered how digital play has converged with the work of media production. From esports and volunteer moderation to play-testing, the circuits of game production are accelerated by players’ passionate engagements as fans and hobbyists, which are intertwined with their professional ambitions to join the industry. It is now taken for granted in scholarly discourse that work and play, production and consumption, and professional and amateur identities are blurring. Researchers propose hybrid terms such as ‘prosumption’ or ‘playbour’ to capture the variation, complexity and contradictions in media participation and value creation across diverse fan practices. This analysis proposes that these post-Fordist neologisms oversimplify techno-cultural changes and legitimate ambiguities in fans’ relationships with media companies and their imperatives for productivism in platform capitalism and its gig economies. In contrast, hobbies have always been a mediating category of productive leisure that can be traced back to industrialization’s cleavage of labour from recreation. This article argues that charting how this liminal category of hobbies has been institutionalized in contemporary media practices provides an analytical lens to interrogate post-Fordist obligations of productivity and neo-liberal expectations of entrepreneurialism.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".