Build Back Better: Challenges, Concerns, and COVID-19 in Canadian Education and Career Development
Bibliographic record
Abstract
This dissertation compiles works that focus on two industries during the COVID-19 pandemic: education and career development. These works address many of the pillars in the UN (2020) Research Roadmap for COVID-19 Recovery, which aims to better understand the effects of the pandemic, and how our understanding of these effects can be leveraged to build back better. This dissertation focuses specifically on how both industries experienced the shift to virtual or online services that took place during the pandemic. To explore this topic this dissertation employs a mixed method, including the use of survey research, focus groups, and statistical analyses such as logistic regression to better understand who was most affected by the realities of the pandemic. This compilation of works highlights how shifts to online or virtual service delivery amplified existing social problems and inequalities, caused strain on individual’s mental health, and offers solutions for proactively future-pandemic or crisis-proofing.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.058 | 0.015 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".