Professional image under threat: Dealing with learning–credibility tension
Bibliographic record
Abstract
How does one learn and build credibility simultaneously? Such is the challenge faced by an increasing number of professionals, who must quickly get to grips with new assignments while displaying sufficient knowledge to be regarded as experts. If they do not, they will be unable to exert influence over the situation. To address this puzzle, we draw on data from 21 months of participant observation during consulting assignments, and interviews with 79 management consultants. Building on Goffman’s notion of face, we identify ‘learning–credibility tension’ – a discrepancy between a newcomer position that requires professionals to learn, and a role-based image that requires credibility – as a salient and costly issue during organizational entry. Specifically, we find that consultants experience threats to their face during interactions with clients. They deal with these threats by performing individual tactics that help them reduce the anxiety associated with learning–credibility tension, and support their relationship with clients. Our study builds theory in socialization by revealing tactics that allow professionals to keep face while seeking the information they require to adjust to new settings. We also contribute to substantive debates on management consulting by relating insights from the sociology of professions to contemporary knowledge workers.
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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 | 0.011 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| 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".