Bibliographic record
Abstract
e wrote this introduction separately from our homes, communicating by telephone and a shared Google document to reduce internet bandwidth requirements so the other people in our families, who include both teachers and K-12 students, were better able to pursue their own work online.Due to the COVID-19 pandemic, we were four weeks into what our governor, Andrew Cuomo, calls "New York on PAUSE," a comprehensive plan to flatten the infection curve that closes all nonessential businesses, prohibits in-person gathering, and encourages social distancing.Our university moved all classes and meetings to digital spaces, so we are learning to use new tools for old purposes and old tools in new ways.Like so many others, we are feeling anxious and uncertain, worrying about loved ones' health, massive unemployment, disproportionate impact on vulnerable populations, and the state of the economy in general.We recognize and are grateful for our own current blessings and privileges, realizing that this could change momentarily.We hope the public health crisis in which we are now immersed has somewhat abated by the time this issue goes to press.Yet, we suspect that we all will continue to be dealing with the fallout from this crisis well into the future.From where we sit, the news isn't all grim, however.Our social isolation has, ironically enough, allowed many of us to connect with one another in new ways.Some families are gathering for games and for collective exercise, whereas they previously struggled, given busy schedules, to be in close proximity.People are figuring out creative mechanisms to mark milestones, celebrate birthdays, and drink cocktails together without putting one another at risk.When we looked at this issue's content, most of which was completed before the pandemic, we saw evidence of connections
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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.763 | 0.693 |
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".