Human Rights, Information, and Communication Technology (ICT): A Case Study of Art Education Teacher Candidates
Bibliographic record
Abstract
Case study research on human rights education is addressed within the context of a pre-service course entitled, digiART and Human Rights: A New Media, Arts Integrated Project. The research was conducted during the fall-winter 2011/2012. The authors drew upon work during a previous 2011 Faculty of Education Summer Graduate Institute and incorporated these concepts into developing meaningful curricula using contemporary technologies. Following Tibbitts (2002) Human Rights Education Paradigm of the (1) values/awareness model (2) accountability model and (3) transformational model investigators found that models 1 and 3 served to enlighten teacher candidates' approaches to teaching human rights issues in which they incorporated ICT. New media processes during the preproduction to postproduction are described. This case study has potential for broader applicability across disciplines and provides incentive for a renewed dialogue about the value of visual arts and art education.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".