Behavior Modification: What it is and how to do it
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The behaviour modification approach - introduction areas of application - an overview basic behavioural principles and procedures - getting a behaviour to occur more often with positive reinforcement decreasing a behaviour with extinction getting a new behaviour to occur - an application of shaping developing behavioural persistance through the use of intermittent reinforcement types of intermittent reinforcement to decrease behaviour doing the right thing at the right time is a matter for stimulus discrimination training developing appropriate behaviour with fading developing and maintaining behaviour with conditioned reinforcement getting a new behaviour to occur with behavioural changing transferring behaviour to new settings and making it last - generality of behaviour change eliminating inappropriate behaviour through punishment establishing a desirable behaviour by using escape and avoidance conditioning procedures based on principles of respondent conditioning some preliminary considerations to effective programming strategies - short-cuts tactics with stimulus control - instruction, modeling, guidance, and situational inducement alternative strategies for decreasing behaviour dealing with data - behavioural assessment - initial considerations direct behavioural assessment - what to record and how doing research in behaviour modification putting it all together - designing a programme to overcome a behavioural handicap token economies helping an individual to develop self-control systematic self-desensitization cognitive behaviour modification areas of clinical behaviour therapy behaviour modification - a rapidly growing concern - giving it all some perspective - a brief history.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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 it