"In Our Own Words": Creating Videos as Teaching and Learning Tools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Online videos, particularly those on YouTube, have proliferated on the internet; watching them has become part of our everyday activity. While libraries have often harnessed the power of videos to create their own promotional and informational videos, few have created their own teaching and learning tools beyond screencasting videos. In the summer of 2010, the authors, two librarians at York University, decided to work on a video project which culminated in a series of instructional videos entitled “Learning: In Our Own Words.” The purpose of the video project was twofold: to trace the “real” experience of incoming students and their development of academic literacies skills (research, writing and learning) throughout their first year, and to create videos that librarians and other instructors could use as instructional tools to engage students in critical thinking and discussion. This paper outlines the authors’ experience filming the videos, creating a teaching guide, and screening the videos in the classroom. Lessons learned during this initiative are discussed in the hope that more libraries will develop videos as teaching and learning tools.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.024 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it