Wunderkind Wisdom: Younger Advisers Discount Their Impact in Reverse Advising Contexts
Bibliographic record
Abstract
Common wisdom suggests that advice typically flows from an older expert to a younger novice. However, disruptions in technology and more fluid organizational hierarchies have made peer advising (i.e. giving advice to others of the same age) and reverse advising (i.e. giving advice to someone older with less expertise) more common. Six studies with MBA students and working professionals explore the psychology of advisers across these contexts. Although advisers believed they would be less effective in reverse advising and more effective in peer and traditional advising contexts, these perceptions were misguided: individuals in reverse advising contexts did not evaluate their advisers as less effective than did advisees in traditional and peer advising contexts. This perception-reality gap is driven by advisers’ perceptions about their own competency in advising others and others’ receptiveness to learning from them. Finally, we demonstrate a reflection-based intervention that mitigates advisers’ misguided beliefs. Taken together, the findings illustrate challenges advisers face in non-traditional advising contexts, and we discuss their theoretical and practical implications on the nascent study of reverse advising.
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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 | 0.012 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".