Personality Science and Self‐Regulation: Personal Projects as Integrative Units
Bibliographic record
Abstract
) make a strong case for enhancing theoretical coherence in the study of self‐regulation by examining recent advances in personality science. I extend their line of argument, reframe their philosophical reminders and strategically shift their suggestions. My goal is to provide an augmented base from which personality science and self‐regulation research and practice can derive mutual benefit. Consensus seems to be emerging that personality science can be usefully conceptualised as a multi‐tier structure, each floor of which focuses on different units of analysis. I focus on Tier I (trait units) and Tier II (PAC units (personal action constructs)). I suggest that Tier II is the home of both the social cognitive theorists and social ecological theorists and that they seem to have colluded to ignore each other. I use Cervone et al.'s timely article as a stimulant to do some renovation work on this floor. Focusing on personal projects I suggest that they provide an integrative function for personality science that augments the contributions of their close neighbors doing CAPS and KAPA research. By some minor renovations we find ourselves able to speak to the narrative theorists in the loft above and even to the trait theorists below. The resulting conversational potential, I suggest, is salutary for both personality science and the study of self‐regulation.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".