Bibliographic record
Abstract
249, 332-333, 389 agglomeration 11, 238, 358 agglomeration (economics, externalities) 245, 252, 286, 410 amenities 153-154 Annual Population Survey 275, 287 Antilleans 217-230 assimilation 52-77 Babel effect 214, 256 big data 25, 45 burglary 201-203 Canada 117, 147, 358 census (block, tract) 208-210 centralization 33-34 checkerboard problem 34, 38, 42 Chicago 204 church attendance 78-88, 95-96 circular migration 59 citizenship 61, 73 clustering 33-34, 37-38, 40-42, 218 cohort 26 Community Well-Being Index 131-133, 137, 139 complements (anti-complements) 53, 276 concentration 22, 28, 37-40, 215-216 country of birth 84, 331, 335, 402-404 creative class 151-152 crime 194 crime rates 152
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 | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.669 | 0.412 |
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".