Bibliographic record
Abstract
I agree with the target essay that psychology has something to offer in helping to address societal problems. Intelligence has helped meliorate some social problems throughout history, including the period of time that is covered by the Flynn effect, but I agree with Sternberg that other psychological characteristics may be contributing as well, particularly increases in rationality. I also believe that increasing human rationality could have a variety of positive societal affects at levels somewhat smaller in grain size than the societal problems that Sternberg focuses on. Some of the societal problems that Sternberg lists, however, I do not think would be remedied by increases in rationality, intelligence, or wisdom, because remedy might be the wrong word in the context of these issues. Issues such as how much inequality of income to tolerate, how much pollution to tolerate, and how much we should sacrifice economic growth for potential future changes in global temperature represent issues of clashing values, not the inability to process information, nor the lack of information, nor the failure to show wisdom.
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".