Identity struggle, professional development and career
Bibliographic record
Abstract
Purpose Through a single‐person career/life history of a human resource (HR) manager, the purpose of this paper is to illuminate the relationship between professional identity and “being” a manager in the context of a “whole life”. Design/methodology/approach The approach of this paper is to use an extended interview with a single HR management professional to consider the complexities and discontinuities of managing a professional and personal life. The interview is considered in light of Alvesson's seven images of self‐identity. Findings Managers are constantly negotiating a professional identity, “being” a manager and a career in the context of their whole life. In developing managers in an increasingly stressful and competitive environment, the intersection between the various elements of a manager's life should be taken into account. Research limitations/implications Closer attention should be paid to the lives of managers in the context of their identity and careers. Research should more closely consider, in detail, the lived experiences of managers and professionals. Practical implications Management development should focus on lived experiences of managers rather than competency and skill development. Managerial performance is related to the coping and sensemaking that occurs within specific contexts and management development professionals should focus more on these aspects of a “managerial life”. Originality/value The value of the paper is in highlighting the importance of very focussed and very personal management development.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".