The Programmer as Player: Uncovering Latent Forms of Digital Play Using Structuration and Actor-Network Theory
Bibliographic record
Abstract
Is programming a game considered play? Normally we would say that it is not; play happens when a game is consumed, not when it is produced. But by adopting this perspective we are falling into the trap that popular culture and mass media set when they categorize games as just another entertainment product. What, then, is the true purpose of this division between programming and play? Why do we seem them as different? In this article, I will explore the early history of computer gaming to show how this dichotomy came about. As it turns out, the computer engineers who worked with the earliest computer systems must shoulder much of the blame. Those programmers who created games such as Spacewar identified themselves as proto-hackers, standing a distance apart from those engaged in more serious computer work. Engineers such as Douglas Engebart, meanwhile, were thinking about the computer as a tool to solve problems, not a platform for artistic endeavour. These two forces enabled outsiders to consider gaming and programming to be wholly separate activities. For the purposes of this work, both structuration theory and actor-network theory are employed. I found that each methdology offered fresh insights into these issues, which could then be merged to provide a complete picture of this early era in digital computing.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".