The pipeline pinball energy thrill ride game: a little theatre in a computer game
Bibliographic record
Abstract
Our world is in a dilemma. We're hooked on hydrocarbons and unless we stop spinning the gears of fossil-fuel dependency we risk being held ransom by what that industry observers call a logic-defying rally based on fear and speculation. (The National Post, August 2005) As human beings, we don't deserve to be victimized by fear factors ranging from Middle-East politics and terrorism to refinery shut downs and turbo-moneymaking bullishness. No one can fully predict the future but it's easy to see that the stakes are high. For the sake of the world economy and our own peace of mind as individuals, we need to pull out all the stops on alternative energy research and development to help spiral our way out of a vicious circle of fear and fossil fuel dependency. This is where Pipeline Pinball Energy Thrill Ride Game comes into being. This lecture demonstration, meant to accompany a viewing of the game design video, intertextualizes the video's script with the action story of the computer pinball game's narrative along pathways of non-linear risks and plot surprises that make it a little theatre in a serious game. Pipeline Pinball Energy Thrill Ride Game game design is part of Lori Shyba Spies in the Oilpatch practice-based PhD dissertation at The University of Calgary, Canada, where the operative inquiry is, How can computer-mediated interactive theatre activate us to better understand our world's natural energy resources?
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".