Adopting a Cloak of Incompetence: Impression Management Techniques for Feigning Lesser Selves
Bibliographic record
Abstract
The “cloak of competence” concept captures attempts to disguise limitations and exaggerate abilities. The author examines the conceptual converse: the “cloak of incompetence,” or the various ways people deliberately disregard, disguise, downplay, or diminish their personal abilities. Drawing on a comparative analysis of manifold empirical cases, the author identifies three generic competence-concealing techniques—avoidance, performance, and neutralization—and considers some of the interactional contingencies that can enhance or reduce their effectiveness. Avoidance and performance techniques are used to manage creditable competence. Neutralization techniques are used to manage credited competence. Each strategy obstructs the appearance and attribution of competence in a particular way: avoidance techniques prevent the dramatic realization of competence, performance techniques dramatically realize incompetence, and neutralization techniques discount, downplay, distance, or otherwise explain away evident but undesirable competent performances. The author concludes by discussing some implications for sociologies of persons, culture, and structure.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".