Bibliographic record
Abstract
This article reports on a study that engaged graduate students from one Canadian university in a knowledge creation project, which produced new evidence and insights regarding pressing socio-political issues of our time. This study resulted in the creation of an instructional application known as the IIF (the Interpretive Imagination Forum), a collaborative video research application for use in higher education courses across the disciplines (e.g., anthropology, history, media studies, philosophy, queer studies, sociology, women's studies). Further, this study resulted in the development of a technology-mediated, hermeneutic tagging technique. IIF was developed as an open-source platform for conducting video research. In keeping with open-source curriculum objectives (OSC), a curriculum framework was developed, which can be used in graduate-level courses (e.g., curriculum foundations, qualitative methodology, critical inquiry). Student participants were invited to add, delete, and modify text annotations or tags, which not only resulted in broader understandings of the themes, theories, and concepts that existed within the videotaped content, but also resulted in the development of a creative and innovative instructional and learning tool. The overarching objective of this study was to circumvent linear or normative qualitative analysis and instead facilitate non-linear, creative, and organic approaches to understanding, analyzing, representing, and disseminating theories and concepts derived from video scholarship.
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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 | 0.033 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".