The Empty Vessel Makes the Loudest Sound
Bibliographic record
Abstract
In a November 2, 2017 CounterPunch Posting and under the title “What’s in a Name? An Invitation to CounterPunchers,” I invited the readers to submit their favorite “nicknames” for as many former and current politicians/public figures as they wished. I also promised to collate the readers’ contributions and to share them with CounterPunchers.\nAs responses from across the United States, including far-away places such as Central Europe, Australia, Scandinavia, Canada, Tunisia, and Morocco trickled in over a period of five weeks, I began to harvest and inventory the plethora of mostly newly minted nomenclatures, most of which have, since late 2016, appeared in CounterPunch and other online blogs and publications.\nBy far, over 90% of the responses include a variegated panoply of nomenclature about Donald Trump, his policies, and his many irksome personal habits, utterances, appearance, and foibles. And, to the best of my recollection, there’s not been a single modern-day U.S. President (since Eisenhower and as far back as memory serves) who’s acquired a vast array of colorful, derogatory, and well-earned lexicon of unflattering monikers.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.105 | 0.047 |
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".