Bibliographic record
Abstract
Index pop-up ads 55-6 and the printing press 27 product placement 56, 164 resembling editorial copy 56, 60 revenue 55, 57-8, 144, 165 and search engines 56, 71 and social media 71 advertorials 99, 154, 160, 163, 164, 167 African-Americans 32 agendas 60 agents 9, 92, 132 of change 77, 86 and deception 137, 138 informational 100, 184-5 moral 4, 20 prospective purposive 96 rational 94, 95-6, 100 and rights 95-8, 99, 151 aggregators 8, 41, 55, 64, 73 blog aggregators 72 and copyright 68 feed aggregators 71 hybrid aggregators 72 and plagiarism 64, 65, 70-2 speciality aggregators 71 user-generated aggregators 71-2 Al Jazeera America 85 Al Qaeda 159-60 Alberta (Canada) Code of Ethics 140 Amazon 56 American Medical Association 126-7 American Society of Newspaper Editors 28-9 analog age 65 anchors 15 ancient Greek philosophers 2, 3, 7, 109, 176, 178, 180, 183 ancient Roman philosophers 183 anonymity 48-9, 86, 128-31 AP see
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.820 | 0.628 |
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".