Bibliographic record
Abstract
algorithmic other, 123-125 algorithmic pricing, 146, 152 personalized pricing, 152 algorithmically controlled mobility net neutrality, 174 bias, 68, 134 discrimination, 67 emotions, 37, 45 gig economy, 151-152, 151 humanization, 123 information discrimination, 171-172 navigational systems, 165 net neutrality, 169 common carriage, 173 social media, 55 speech recognition, 88 streaming service recommendations, 91 technological autonomy, 70 young persons' experiences, 119 artificial intelligence, 81-82 automation, 51, 55 behavioural prediction, 83 emotions, 45 poetry, 137 facial recognition, 67 fraud detection, 195 health insurance, 84 in-car navigation, 167 machine learning agnosticism, 198 health diagnoses, 90 speech recognition, 86 machine-written language, 45 military, use by, 55 online advertising, 84, 153-154 platform cities, 15, 22 popular culture, 46-54, 56-57 robots, 36 romantic relationships, 53
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.757 | 0.538 |
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".