Bibliographic record
Abstract
In the contemporary era of Web 2.0, high-tech consumer culture is increasingly engaged in the production of ‘user-generated content’ (UGC) for digital multicast. The tension between global homogeneity and the potential of technology to support multiple voices, histories and viewpoints is of central interest. The new DIY craft movement is successfully adopting Internet technologies to go straight to market as the digital generation increasingly engages in analogue craft practice. The swell of interest in craft values, both in objects and in hands-on feel and process exhibited in blogs such as Wonderland and distribution aggregators like Etsy, offers a productive frame that connects the digital and the analogue. Whether this reveals any anxiety about the intangibility of the digital or points to an increased creativity inspired by UGC remains open to question. The ‘feedback loop’ (to use Schechner’s (2002) term for the connection between an individual’s behavior and what they observe on street, stage and screen) between digital and real world practice, although far from literal, provides a frame for the dialogue between game form and culture at large. This paper teases out aspects of this feedback loop using examples from Sony’s PS3 series Little Big Planet (2008). The argument presented here does not deal with narratological or ludic structures and only tips its hat at the much broader field of fan culture but foregrounds context, style and characterization in its approach to analysis. The rationale for this approach is two-fold; first through the weight Media Molecule, developers of the game, give to visual communication and secondly through the prioritization of the invitation to create over and above the provision of a full triple-A title more typical of a console launch game.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".