“I Feel Quite Hopeful that My Future Is Still Going to be Okay”: Educational Aspirations During COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has radically altered how we learn, work, and live. This qualitative research aimed to study the effects of the COVID-19 pandemic on the educational and occupational aspirations of young Canadian adults. All close to 29 years of age, sixteen participants took part in one-on-one semi-structured interviews conducted through Zoom. Questions probed participants’ hopes, dreams, and perceived obstacles regarding school and work. Coding was completed using the research software Dedoose. Thematic content analysis was performed using both deductive and inductive approaches. Three themes emerged: the benefits and drawbacks of working and learning from home; financial changes and concerns; and hope and optimism despite challenges posed by the pandemic. Working and learning from home were discussed by 88% of participants, making it the most prominent theme. Participants generally agreed that working and learning from home had many benefits, but some expressed concern about the quality of online education. In addition, the pandemic caused financial hardship for a few participants, forcing them to delay educational or occupational plans. However, the majority (75%) expressed positivity and hope for the future. Overall, although the timeline of some participants’ educational or occupational plans changed, their aspirations largely remained the same.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".