Bridging the Gap Between Marketing Education and the Marketing Profession
Bibliographic record
Abstract
Professionals and scholars have discussed the unprecedented pace of change currently experienced by businesses. The dynamics facing business today offer rich insight into the challenges facing university graduates. In this chapter, the authors apply a dynamic capabilities (DCs) view of new graduate employability. Dynamic capabilities theory is rooted in the resource-based view that posits organizations create a competitive advantage by acquiring or developing resources that are rare, valuable, and hard to imitate and replace. They argue that employability can be viewed as the complex integration and application of four specific DCs: (1) intelligence resources, (2) personality resources, (3) meta-skill resources, and (4) job-specific resources. The authors view new graduate competitive advantage as dependent on the ability of university graduates to mobilize and exploit the linkages of these resources throughout their university study years. In adopting these resource categories, they build on previous work and propose a conceptual model to evaluate a new graduate's competitive position in an employment marketplace. In this chapter, the authors provide a prescription for how educators and students can apply an integrated dynamic capability view of new graduate employability to support the professional development of marketing students through the development of a comprehensive personal product roadmap.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".