Bibliographic record
Abstract
Perhaps surprisingly, psychology has been a discipline eager to capitalize on the application of computers for teaching. Traditionally, this has been for statistical calculations, and the presentation of experimental stimuli and the automatic collection of timed events (e.g., reaction times, choice-decision times). Here, the traditional capabilities of computers are being exploited - namely, their accurate temporal sequencing, graphical performance, and, above all, their number crunching. As such, they have been powerful and essential tools for those involved in the more psychophysical or cognitive areas of psychology. Computer-based learning (CBL) remains very much a preserve of these more formal domains. The arrival of hypermedia has opened the way for CBL to be exploited within the less formal domains of psychology; but the level of interactivity is usually very restricted, and the constrained presentational styles means that even this technological progression fails to meet the contextual richness needed in the teaching of much of the behavioural sciences. The advent of multimedia has for the first time provided the potential to explore, within the normal undergraduate learning environment, real behaviour using the observational techniques that form the basic methodology of the practising social psychologist.DOI:10.1080/0968776960040103
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.023 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".