Through the looking-glass: PsycINFO as an historical archive of trends in psychology.
Bibliographic record
Abstract
Those interested in tracking trends in the history of psychology cannot simply trust the numbers produced by inputting terms into search engines like PsycINFO and then constraining by date. This essay is therefore a critical engagement with that longstanding interest to show what it is possible to do, over what period, and why. It concludes that certain projects simply cannot be undertaken without further investment by the American Psychological Association. This is because forgotten changes in the assumptions informing the database make its index terms untrustworthy for use in trend-tracking before 1967. But they can indeed be used, with care, to track more recent trends. The result is then a Distant Reading of psychology, with Digital History presented as enabling a kind of Science Studies that psychologists will find appealing. The present state of the discipline can thus be caricatured as the contemporary scientific study of depressed rats and the drugs used to treat them (as well as of human brains, mice, and myriad other topics). To extend the investigation back further in time, however, the 1967 boundary is also investigated. The author then delves more deeply into the prehistory of the database's creation, and shows in a précis of a further project that the origins of PsycINFO can be traced to interests related to American national security during the Cold War. In short: PsycINFO cannot be treated as a simple bibliographic description of the discipline. It is embedded in its history, and reflects it. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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 | 0.019 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.045 | 0.146 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.028 | 0.039 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.190 | 0.168 |
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".