Looking into the dark-side of the incremental theory: when incremental worldviews hurt
Bibliographic record
Abstract
What happens when one has repeatedly persisted at a task, but with all efforts met \n with disappointment? Does this mean that one has purely not expanded a sufficient amount of \n effort? How do we qualify or measure the “right” amount of effort? Can we even qualify and \n measure the “right” amount of effort? And when exactly are we finally satisfied with the \n amount of effort we expand, or does this amount of effort not reached so long as we do not \n achieve our goals? This project aims to explore how the rigid association between effort and \n success in some versions of incremental thinking can lead people astray: When the absence \n or lack of effort is attributed unjustifiably to failures (e.g. poor grades, unable to achieve an \n important life goal). In this study, we compared two different cultures – East Asians and \n North Americans – on how they differed from each other in two different scenarios – \n average-effort and exceptional-effort. Participants were recruited from Singapore (n=89) \n and Canada (n=319) respectively. Results that are consistent with existing literature includes \n the more intense negative and fear affect experienced by Canadians in comparison to \n Singaporeans, as well as the negative correlation between grit and goal disengagement. This \n study aims to look at the dark side of the incremental theory despite having existing literature \n champion its advantages in various learning approaches. We have also included a section on \n implications, limitations and future directions.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".