Bibliographic record
Abstract
Purpose The purpose of this paper is to identify key developmental relationships for career‐spanning success and to examine relational models and support expectations associated with these relationships. The paper creates propositions associating developer‐protégé schema congruence and incongruence to relevant outcome variables. Design/methodology/approach Study 1 employed qualitative coding of developers identified in 77 hall of famer induction speeches and Study 2 used a cross‐industry survey of 425 respondents to assess the relational model and support expectations associated with the seven most highly‐cited developer roles from Study 1. Findings Study 1 identified these highly‐cited developer roles as a CEO, manager, work teammate, friend, spouse, parent, and unmet hero/idol. Study 2 described the expected relational models associated with these roles and found significant differences in the relational model and support expectations associated across roles. Research limitations/implications While study 1 focused on a primarily male sample using retrospective data, it generalized and extended previous research on key developer roles for extraordinary career achievement. Based on the key findings from study 1, study 2 surveyed respondents regarding developer role expectations rather than expectations of particular developer‐protégé relationships. Practical implications These findings identify how and with whom protégés should consider initiating and fostering key developmental relationships to enhance their networks while broadening and deepening organizations' understanding of the importance of their members having a variety of organizational and non‐organizational developers. Originality/value These findings challenge the notion that developer‐protégé relationships fit a “one size fits all” reciprocal exchange motif as it is the first study to explore expectations associated with key developer relationships using relational models theory.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.527 | 0.479 |
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".