Bibliographic record
Abstract
This article is a digital video design ethnography describing the first phase of introducing a perspectivity meme into a classroom. A meme is an idea that spreads throughout a system. A perspectivity meme is the idea that people who share their viewpoints and interpretations will gradually affect role changes in the learning environment. They will not only ‘see’ each other's points of view, but also share roles and viewpoints. Digital video technology is the medium that enables the perspectivity meme to spread throughout this learning culture. Educators and junior high school students use digital cameras and computers to record, reflect, present, discuss, and debate. In this description, I present a detailed narrative of a day in the life of learners, a teacher, and three researchers. I describe how a perspectivity meme was planted in a culture. I describe how the community began to think more deeply and personally about the curriculum. In closing, I discuss the importance of sharing roles in the learning and teaching process, thereby activating incremental changes to the learning environment. ORION™, an experimental tool for digital video analysis—previously known as WebConstellations and Constellations—is used to present interactive video data to readers of this article, on the web. ORION supports online collaborative organization, analysis, and presentation of video segments and clusters and can be used by researchers, teachers, and/or learners. Readers are invited to participate online at http://orion.njit.edu in the Burnsview Galaxy.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".