Bibliographic record
Abstract
A recent post on The Scholarly Kitchen website got me thinking about the role of social media in my professional life. I have young adult children who keep me in the loop about social trends to some extent. They instructed me to join Facebook many years ago and, since then, I have remembered friends' and relatives' birthdays with much greater success. I joined Twitter and, for a time, had a personal as well as professional account. It was difficult to keep those separate, and I eventually stopped tweeting on the personal account. I only use my professional one now and with greater circumspect (no politics and fewer complaints about hotels and airlines). I have an Instagram account where I follow many chefs, winemakers, and artists, and I enjoy the beautiful images that they post.
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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.027 | 0.024 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".