Don’t Want to Look Dumb? The Role of Theories of Intelligence and Humanlike Features in Online Help Seeking
Bibliographic record
Abstract
Numerous studies have shown that individuals' help-seeking behavior increases when a computerized helper is endowed with humanlike features in nonachievement contexts. In contrast, the current research suggests that anthropomorphic helpers are not universally conducive to help-seeking behavior in contexts of achievement, particularly among individuals who construe help seeking as a display of incompetence (i.e., entity theorists). Study 1 demonstrated that when entity theorists received help from an anthropomorphized (vs. a nonanthropomorphized) helper, they were more concerned about negative judgments from other people, whereas incremental theorists were not affected by anthropomorphic features. Study 2 showed that when help was provided by an anthropomorphized (vs. a nonanthropomorphized) helper, entity theorists were less likely to seek help, even at the cost of lower performance. In contrast, incremental theorists' help-seeking behavior and task performance were not affected by anthropomorphism. This research deepens the current understanding of the role of anthropomorphic computerized helpers in online learning contexts.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".