Bibliographic record
Abstract
This paper describes cognitive ethnography as a method of choice for game studies, multimedia learning, professional development, leisure studies, and activities where context is important. Cognitive ethnography is efficacious for these activities, as it assumes that human cognition adapts to its natural surroundings (Hutchins, 1995, 2010) with emphasis on analysis of activities as they happen in context; how they are represented; and how they are distributed and experienced in space. The methodology is described for increasing construct validity (Cook & Campbell, 1979; Campbell & Stanley, 1966) and the creation of a nomological network (Cronbach & Meehl, 1955). This description of the methodology is contextualized with a study examining the literate practices of reluctant middle school readers playing video games (Dubbels, 2008). The study utilizes variables from empirical laboratory research on discourse processing (Zwann, Langston, & Graesser, 1996) to analyze the narrative discourse of a video game as a socio-cognitive practice (Gee, 2007; Gee, Hull, & Lankshear, 1996).
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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.
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".