Professional image under threat: Dealing with learning–credibility tension
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it