What do we manipulate when reminding people of (not) having control? In search of construct validity
Bibliographic record
Abstract
The construct of personal control is crucial for understanding a variety of human behaviors. Perceived lack of control affects performance and psychological well-being in diverse contexts - educational, organizational, clinical, and social. Thus, it is important to know to what extent we can rely on the established experimental manipulations of (lack of) control. In this article, we examine the construct validity of recall-based manipulations of control (or lack thereof). Using existing datasets (Study 1a and 1b: N = 627 and N = 454, respectively) we performed content-based analyses of control experiences induced by two different procedures (free recall and positive events recall). The results indicate low comparability between high and low control conditions in terms of the emotionality of a recalled event, the domain and sphere of control, amongst other differences. In an experimental study that included three types of recall-based control manipulations (Study 2: N = 506), we found that the conditions differed not only in emotionality but also in a generalized sense of control. This suggests that different aspects of personal control can be activated, and other constructs evoked, depending on the experimental procedure. We discuss potential sources of variability between control manipulation procedures and propose improvements in practices when using experimental manipulations of sense of control and other psychological constructs.
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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 | 0.055 | 0.284 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".