Developing and launching effective and engaging videos without breaking the bank
Bibliographic record
Abstract
We have leveraged a number of free or cheap resources (and the occasional not-so-cheap resource) to create, distribute, promote, and update videos to promote and educate about library resources and services. These videos can be created by almost anyone with a bit of patience and willingness to learn. This presentation will essentially be a lesson in creating effective videos, quickly and inexpensively. It will focus on practical instructions and demonstrations, with framing information on pedagogy and the legal/moral issues we have encountered. In brief, the lesson cover six steps: 1. Planning the video. 2. Creating and gathering the raw material for the video: digital photos and images, screencaps, videos, links, text-based files, etc. 3. Recording a screencast with voice recording. 4. Editing the screencast and voice recording. 5. Posting and promoting the finished video. 6. Updating videos with new or changed information. Our presentation offers attendees practical ways to create videos that do not require significant monetary investments, while demystifying the creation process for even rudimentary experts. All materials and links, as well as supplmentary information, is available on our session Prezi: http://bit.ly/141eQZo
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.132 | 0.053 |
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".