Virtual Convergence: Exploring Culture and Meaning in Playscapes
Bibliographic record
Abstract
Background Research into digital practices and cultures repeatedly calls attention to the complexity of communication spaces and meaning-making practices. With the blurring of boundaries between online and offline, these entangled practices involve the interweaving of human, material, semiotic, and discursive practices. Purpose This introductory article builds on theoretical work by Huizinga and Appadurai and presents the concept of playscapes to help situate the overall collection of articles in this special issue, Virtual Convergence: Synergies in Virtual Worlds and Videogames Research. Research Design This analytic essay examines virtual worlds and videogames and offers the concept of playscapes to expand the discourse about space and finitudes of practice. Conclusions Playscapes extend current conversations about learning, transmedia, and play ecologies because playscapes can support the discussion of entangled meaning making across space and time, all the while acknowledging the situated nature of the activity.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".