Bibliographic record
Abstract
: This chapter explores process and outcomes paradigms in media arts pedagogy in the context of new media information and communications technologies (ICTs) and the “contested” Internet – the highly controlled, surveilled, and commercialized networks we use. The paradigmatic notions of pedagogy as either process-based (Dewey) or outcomes-oriented (Niebuhr) are discussed in connection to studio-based learning. Process-based approaches to education have been dominant in the twentieth century and were developed by Dewey, Vygotsky, Schön, Kolb, and many others, while outcomes-oriented education developed later in the period. Originating with sociologist William Spady, outcomes-based education (OBE) (1994) has been implemented as an administrative tool both for measuring individual performance as well as course or program level assessments. Performance “metrics” in outcomes initiatives emphasize peer review and measurement, de-emphasizing flexible approaches such as allowing individual students to assess according to their own goals. This chapter draws on Niebuhr rather than Dewey for expanding OBE frameworks for the use of ICTs in media arts project-based learning. Students are technologically enabled in project-based learning to become agents of their own goals and learning agendas since ICTs provide capabilities for individually tailored learning experiences which contextualize knowledge and assist in developing informed Internet citizenship.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".