On Deconstructing Commentaries Regarding Alternative Theories of Self-Regulation
Bibliographic record
Abstract
The present commentary addresses issues raised in four replies to my editorial on the functional properties of perceived self-efficacy (Bandura, 2012). In my comments on the paper by Jackson, Hill, and Roberts (2012), I discuss the arbitrary nature of “disposition” and question whether an essentially atheoretical computer-structured inventory based on a mixture of superficially assessed habitual behaviors constitutes a theory of personality. In another set of comments, which speak to the paper by Vancouver (2012), I identify two major flaws in Powers’ (1991) perceptual control theory and document experimental compromises in Vancouver’s efforts to demonstrate that goals and self-efficacy operate counteractively. My comments on the Yeo and Neal (2013) paper center on their unsuccessful efforts to explain and verify the proposition that general and specific self-efficacy work at cross-purposes. In response to Bledow’s (2013) entry, I address the conceptual ambiguity of his theory of unconscious self-motivation, misconstruals of the role of self-efficacy in the process of change, and marginalization of the functional role of consciousness in human behavior.
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 | 0.046 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.035 | 0.063 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".