Bibliographic record
Abstract
The following six diary entries and journaling pieces were written in the months after the 2019 Coronavirus pandemic (COVID-19) hit Canada. They express my attempts to pinpoint causes of my personal unhappiness and how overwhelming expectations of productivity affected my mental health and relationships during quarantine. My diary entries are in chronological order, containing reflections on my birthday in quarantine, notes about racial injustice, and reminders of self love. I delve into how isolation and social distancing shifted my perspective, creating unexpected goals, and new hobbies. These reflections reveal thoughts of uncertainty about my career and concerns about my friendships. As the entries continue, themes of self-acceptance and confidence emerge. I encourage readers to empathize with these conflicting feelings of what is “normal” or “expected” during adulthood; challenge destructive behaviour, including excessive self doubt and self-sabotage; and form uplifting habits for a better sense of self. My words have been taken directly from my personal journal and left as they are, no editing, no revising, no polishing. This composition creates an open space that acknowledges how emotions of guilt, anxiety, low self-image, sadness, and hope defined so much of our post-COVID-19 reality.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.191 | 0.057 |
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".