Bibliographic record
Abstract
COVID-19 has created a new reality for countries worldwide as leaders are tasked with the responsibility of enacting safety measures to stop the rate of infection. Social distancing is promoted as one of the main ways of curbing the spread of the virus. Such measures limit social interaction and the spaces people are free to occupy. The following poem, entitled “Sitting in the dark: COVID-19 and mental well-being” speaks to the mental health impacts of such closures on the youth population, highlighting that no one is immune from the virus. This poem also explores the interconnectedness of a person’s physical and mental health andthe subsequent need to pay attention to both realities during times of global crisis. Despite the challenges the pandemic presents, it is imperative that youth find an outlet to cope, one that will help them develop resiliency and a sense of hope.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.056 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".