Bibliographic record
Abstract
Professor Inglehart and I are involved in a foreground/background dispute. We see the same black-and-white image (Figure 1) but interpret it differently. Inglehart’s foreground is white, leading to him to conclude that the image portrays two faces. My foreground is black, leading me to conclude that the image portrays a goblet. His foreground (my background) consists of the intergenerational causes of value change, notably socialization in relatively peaceful and prosperous times and the concomitant proliferation of higher-status occupations. My foreground (his background) consists of geopolitical rivalry and growing income inequality, forces that push the citizens of today’s Great Powers away from postmaterialism and into the camp of the meaner angels of our nature. True, we can see each other’s foreground — I adduce data showing that young Chinese citizens are more postmaterialistic than their elder compatriots; Inglehart admits that growing geopolitical rivalries and income inequality have stymied Russia’s advance to postmaterialism — but we each insist that our foreground is the main story.
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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.076 | 0.084 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".