Gaming with the Subaltern
Bibliographic record
Abstract
Share on Gaming with the Subaltern: Workshop on Diversity and Inclusion in Games Authors: Cale J. Passmore University of Saskatchewan, Saskatoon, SK, Canada University of Saskatchewan, Saskatoon, SK, CanadaView Profile , Regan L. Mandryk University of Saskatchewan, Saskatoon, SK, Canada University of Saskatchewan, Saskatoon, SK, CanadaView Profile , Sarah Schoemann Georgia Institute of Technology, Atlanta, GA, USA Georgia Institute of Technology, Atlanta, GA, USAView Profile , Daniel Gardner University of California, Irvine, Irvine, CA, USA University of California, Irvine, Irvine, CA, USAView Profile , Cayley MacArthur University of Waterloo, Waterloo, ON, Canada University of Waterloo, Waterloo, ON, CanadaView Profile , Mark Hancock University of Waterloo, Waterloo, ON, Canada University of Waterloo, Waterloo, ON, CanadaView Profile , Mahli-Ann Butt University of Sydney, Sydney, NSW, Australia University of Sydney, Sydney, NSW, AustraliaView Profile , Theresa Jean Tanenbaum University of California, Irvine, Irvine, CA, USA University of California, Irvine, Irvine, CA, USAView Profile Authors Info & Claims CHI PLAY '18 Extended Abstracts: Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play Companion Extended AbstractsOctober 2018 Pages 695–701https://doi.org/10.1145/3270316.3271552Published:23 October 2018Publication History 0citation212DownloadsMetricsTotal Citations0Total Downloads212Last 12 Months35Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
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 | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.013 |
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".