Assessing an organization's preparedness for the virtual enterprise: the TEMPLET model
Bibliographic record
Abstract
The CALS community has been developing virtual enterprise tools since the mid-1980s. This paper discusses the development of a model of an organization's virtual enterprise capability that draws on the experience and perceptions of the CALS practitioner community. The model, called the TEMPLET model, is a hierarchical model with four main capability elements: technology, information management, organization and process. Specific capabilities for each element are defined. The model was verified through a practitioner survey and a set of field studies. The relative importance of each element and item is discussed. Adjustments to the model are proposed as well as a reduced model that could be used to gain a quick picture of an organization. The TEMPLET model is of interest to practitioners as a model of an organization's VE capability. For the academic community, it provides a model that can be used to develop VE assessment and improvement capability tools. For both communities, it summarizes years of CALS experience and how it impacts on our understanding of the development of virtual enterprises.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".