Changes in the Style and Contents of Abstracts from The Journal of Consulting and Clinical Psychology between the 1960s and the 2010s
Bibliographic record
Abstract
This study was conducted to examine changes in the style and content of abstracts from the Journal of Consulting and Clinical Psychology across time. Characteristics examined were word commonness, word activation, word pleasantness, sentence length, abstract length, mentions of inferential statistics and mentions of drugs (both street drugs and pharmaceuticals). Abstracts (N=510) were downloaded from volumes published before the wide introduction of computers (1968-9) and from those published in more current years (2016-17). Scores for word pleasantness and word activation were assessed with the Dictionary of Affect in Language. Word commonness was scored in comparison to a corpus of everyday English, and sentence length and abstract length were measured in terms of number of words. There were several strong and significant differences between abstracts from the pre-computer era and those from the 21st century, including greater length, more mentions of inferential statistics and more mentions of drugs in the later time period. A stepwise discriminant function analysis was able to correctly predict the origin (early or pre-computer versus 21st century) of 98% of the abstracts on the basis of the characteristics measured (canonical correlation=.89).
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 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".