Bibliographic record
Abstract
Purpose: To create and produce video recordings, with or without sound, of a computer screen that can be shared online.Updates: Jing provides updates as needed.Screenr is web based, so updates are not required.Compatibility: Jing (downloadable): Windows XP, Vista, Windows 7, or Windows 8, and Microsoft.NET Framework 4.0 Full, Mac OS X 10.6.8 or later, and QuickTime 7.5.5 or later (as stated on website).Screenr (web based): Mac (OSX 10.4 and up) or Windows (XP, Vista or Windows 7).Supported by common browsers including IE 6, 7, and 8; Firefox 3 '; Safari 3 '; and Google Chrome.Requires Java 1.5 runtime or later for recording and the Flash Player 9 (release 115 or later) or Flash Player 10 for playing screencasts (as stated on website).Cost: Free.Both Jing and Screenr have upgraded account options, with added features such as increased storage space (with Screencast.com Pro, Jing's hosting plat- form) or privacy control features (Screenr Business), for a monthly fee. Pros: Free. Jing has a screen capture function that allows screen captures to be annotated. Privacy control features can be enabled with screencasts uploaded and shared through Screencast.com. Screenr can be accessed from any computer, as it is web based. Cons: Both Jing and Screenr limit recording time to five minutes per screencast. Neither software program will allow editing or adding special effects, such as callouts, zooms, and highlighting. There are no quizzing options. Jing's hosting platform (Screencast.com)has a limited storage space of 2 GB and monthly bandwidth of 2 GB.Screenr does not have a privacy control feature to allow screencasts to be hidden or password protected.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.244 | 0.069 |
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".