Bibliographic record
Abstract
Ever since the Coronavirus, COVID-19, pandemic hit in early 2020, most, if not all, institutions have been affected due to the mass amount of lockdowns worldwide. Education systems across Canada and the globe have experienced multiple impacts of the COVID-19 (Azzi-Huck & Shmis, 2020). On March 13, 2020, Toronto’s Ryerson University transitioned from in-person delivery to online delivery (Ryerson University, 2020). As we continue to learn in virtual settings, the question I raised is: How has online learning affected students, faculty, and staff members during a global pandemic? This project will show the myriad ways in which university individuals were impacted when their education transitioned from in-person delivery to an online/virtual setting during a global pandemic. Throughout the paper, the major research project will try to achieve a more robust and deeper emotional understanding of how technology affects individuals’ creativity, productivity, social interactions, and mental and physical wellness in their daily functionality. The approach for this project will be gathering qualitative interviews about various post-secondary institution’s experiences. Instead of a case study, the project will employ a journalistic lens by conducting interviews that will include students currently studying under the umbrella of The Creative School. Through my reporting and my research, I will present the findings through a work of journalism.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".