Bibliographic record
Abstract
I wrote this book as a text for intermediate and advanced level courses in motivation, and as supplementary material for courses on comparative psychology and biopsychology. Although there may be overlap with material in other current textbooks on motivation, the approach and treatment taken in this one is quite different. It does not present an exhaustive review of facts and anthology of theories in the field, but instead, attempts to cover selected material linked in a coherent fashion. In doing so I have attempted to make some sense of the diverse range of topics that are covered in other motivation texts. I have also attempted to indicate the interplay of material on animal and human research, and hope that the reader will find the presentation a natural one in which the transition between the two appears unforced. The organisation of each of the substantive chapters begins with a consideration of ‘classic’ theories and studies of a specific motivated activity, and is followed by discussion of selected current developments indicating further complexities of the issue. Even though some earlier theories have been superseded by recent models, like Mook (1996), I believe that students will benefit from such exposure, and consequently, develop a better understanding of how current models and research evolved. Shortly after I had completed this book, I encountered others offering new insights that were not available during my preparation. Within the limited time remaining for the production of this book, I have attempted to fine-tune some of my presentation with some ideas that I have learned from these new works.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.240 | 0.162 |
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".