Bibliographic record
Abstract
Nota bene: This introduction was written near the end of 2020, a year that saw the world struggle with COVID-19. These issues make up the primary body of the below text. Yet, as we moved into the new year, perhaps thankful that 2020 had come to a close, on 6 January, and before the introduction was sent to publication, the US Capitol building in Washington, DC, was laid siege by far right extremists, White supremacists, and supporters seeking to stop the confirmation of the election of Joseph Biden. I [Frank] am reminded of a similar note I wrote in an article for the Sexual Violence Research Initiative’s “16 Days of Activism” series in early December: “We write this post amidst political protests that have shaken Kyrgyzstan, with the recent election results being annulled. We send our thoughts for those working to ensure a fair, democratic, and transparent government; and hope for a speedy resolution to these issues” (Kim and Karioris 2020). In a similar sense, with the events still etched in our minds and processes just beginning to begin (arrests, an impeachment, etc.) and the inauguration still to come, we include this short note affirming our commitment to democratic principles, challenging violent masculinity, and supporting antiracist activism.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.594 | 0.446 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".