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Record W2270771794

Ramping up Research as Writers and Readers

2008· article· en· W2270771794 on OpenAlexaffabout
Jennifer Jenson, Suzanne de Castell

Bibliographic record

VenueLoading... · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsSimon Fraser UniversityYork University
Fundersnot available
KeywordsDisciplineDramaGenerative grammarEthnographySociologyField (mathematics)Cultural studiesEpistemologySocial scienceAestheticsMedia studiesEngineering ethicsPedagogyVisual artsAnthropologyLinguisticsEngineeringArt
DOInot available

Abstract

fetched live from OpenAlex

The third issue of Loading... demonstrates the strong promise of an emergent, trans-disciplinary field that is drawing on disciplinary work from philosophy, psychology, education, film studies, cultural studies, literary studies, drama, and ethnography, among others. The result is a collection of papers that challenges readers to think, read, play, theorize and, most significantly, research and study games differently as the perspectives we bring individually to our work evolve through generative exposure to one another’s ideas and frameworks. This has been the larger purpose of Loading... and remains the primary indicator of its utility for the development the Canadian game studies community.

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 imitation

Not 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.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.099
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.009
Science and technology studies0.0270.069
Scholarly communication0.0520.060
Open science0.0060.029
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.0120.002

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.

Opus teacher head0.154
GPT teacher head0.402
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2008
Admission routes2
Has abstractyes

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