Bibliographic record
Abstract
Purpose In this study, the authors explore the boundaryless careers of faculty and adopt the intelligent career framework to examine success factors for academic careers. Design/methodology/approach The authors conducted a field study of 36 researchers in the management information systems field from 22 institutions in the US, Australia and Canada. The authors selected the participants representing four strata of researchers: luminaries (high expertise status and high citizenship behavior), experts (high expertise status but low leadership roles), statesmen (low expertise status but high leadership) and journeymen (low expertise status and low leadership). Data regarding the participants' experience of social relationships and social resources as well as entrepreneurial motivations were collected and analyzed. Findings Results show that faculty who “know-why”, “know-how”, and “know with whom” possess socially valued resources and are successful in advancing their careers. They establish high social status and exercise power within their networks to mobilize resources that promote their careers. On the other hand, faculty who fall short of these competencies impose social closure on themselves and do not strive to exploit resources available through their contacts. The study advances a number of theoretical propositions to guide future research on boundaryless intelligent careers. Social implications Social relationships and social resources do not substitute individual competence, leadership and entrepreneurial motivations; individuals need to develop competence valued by their professional communities and exploit available opportunities and assume leadership roles in order to effectively establish instrumental relationships and mobilize social resources to achieve career advancement. Originality/value In this study, we attempt to extend career development research through an examination of the bidirectional relationship between know-why, know-how and know-who in academia.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".