Bibliographic record
Abstract
An essential dimension of the journalism school experience is the hands-on training that takes place in studio courses and workshops.Learning to troubleshoot and adapt in real-time situations is as important as gaining experience with industry-standard equipment.As useful as video conferencing platforms like Zoom and Google Meet have been for some forms of remote teaching, they are not ideal solutions to replicate the collaborative experiential learning that happens in our campus newsrooms and broadcast studios.Through much exploration and experimentation with different digital platforms, instructors and technical staff at the Toronto Metropolitan University School of Journalism were able to continue leading live TV and radio news courses and real-time training workshops while our campus largely remained shuttered from Spring 2020 and into 2021.We sought out tools that were agile, accessible and as user-friendly as possible.Many of these tools have proven to be so effective and versatile that we continue to use them even as students return to classrooms, offering more hybrid approaches to broadcast journalism education.In this commentary, we discuss some of these tools and show how they work in short videos, with the goal of supporting other journalism or media instructors building collaborative virtual newsrooms and studios simply and quickly.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.023 |
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".