Talent Management of Transdisciplinary Roles in Digital Projects: Designing a Business Technology Management Body of Knowledge.
Bibliographic record
Abstract
The acceleration of Digital Transformation has led to rapidly evolving transdisciplinary IS-IT professional roles operating across technology and business units. Digital projects require new tasks and skillsets, staffing requirements, and career progressions continuously updated by organizations of all sectors. IT Talent Management (TM) requires a more integrative and adaptive competency framework to help guide IT professionals in becoming new digital leaders. Accordingly, a unified Body of Knowledge (BOK) is developed to improve TM accuracy, breadth, and flexibility. Entitled Business Technology Management (BTM), it serves as a common language to integrate professional standards and match talents to projects. The first iteration results are presented such as the BTM BOK meta-model, methodology, and development tools. The impacts on digital project leadership practices and ontology-driven design methods are outlined.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".