Cultivating a community from collaboration: The secret to a strong vertical video strategy and system
Bibliographic record
Abstract
The University of Toronto Mississauga (UTM) central social media team has developed an overarching strategy and system over the course of three years, from 2020 to 2023, to help improve their vertical video production while building a community of practice with other internal campus content creators. This case study will discuss the benefits for campus content collaborators. It identifies three types of social video collaborations employed by UTM communications staff to meet the unique requirements and capabilities of different teams. Pulling from the UTM social media team's findings, this paper shares a formula for success used to create vertical videos that improve key performance indicators while building capacity among practitioners. By highlighting strategic practical tips to utilise when creating higher education video content, the authors aim to illuminate the steps involved for those wishing to set up a similar system and strategy. This case study concludes with a reflection on the lessons learned and pitfalls to avoid when collaborating with other campus content creators.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.023 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".