How General Managers Humanize Their Profession: Evidence from Practice
Bibliographic record
Abstract
Abstract: There is a prolific strand of executive literature that analyzes managers ´ daily activities. Nevertheless, research has remained silent about the managers ´ priorities that drive those numerous activities. The goal of this paper is, precisely, to gain a better understanding of which are those manager´s priorities, and to discover which is the place that occupies in those priorities the so called “humanizing dimension of management”. We base our study on an inductive analysis of in-depth interviews with 19 general managers of multinational firms. The analysis results in more than 200 priorities that are clustered in four broader groups of major managerial concerns: to develop the business model and the future of the firm, to manage the people, and to create an institutional strategy –composed of principles and values, purpose, working philosophy- that gives coherence to the organization. Far from what recent claims suggest, our analysis reveals a true preoccupation of executives to integrate a “humanistic dimension of management ” with their daily practice. Maybe academia has unfairly contributed to the delegitimation of management as a profession. Maybe it is scholars that must start the process of humanizing their theories. 2
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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 | 0.022 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".