The Scientometric Structure of Applied Behaviour Analysis
Bibliographic record
Abstract
Social network analysis provides the basis for graphic representation and quantitative analysis of the interactions of complex social systems. Here we report on a new method for using social network analysis to study patterns of scientific activity and potential influence of research interaction groups – i.e., researchers who publish jointly –. Next, we discuss the application of this method to a leading journal in the field of applied behaviour analysis. Data on authors of co-authored articles in the Journal of Applied Behavior Analysis (JABA) and two comparison journals – the Journal of Consulting and Clinical Psychology (JCCP), and the Journal of the American Academy of Child and Adolescence Psychiatry (JAACAP) – published from 2000 to 2010 inclusive were considered to form a social network in a sociometric, or “scientiometric,” analysis. The analysis identified a small number of potentially influential groups or communities of authors publishing in JABA. It appears from this analysis that one of these research groups in particular has the potential to influence research trends in applied behaviour analysis (as represented by JABA authorship interactions). The research interaction groups are not highly specific in terms of the behaviours, procedures, or populations that they frequently deal with. The general scientometric structure found for JABA, consisting of one or two highly influential research interaction groups, no more than 10 active non-influential research interaction groups, and a mass of non-influential micro-communities, was similar to that in the comparison journals looked at in this study.
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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 | 0.067 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.083 | 0.071 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".