Assessing the assessor: a framework for BIM maturity, capacity, and competency evaluation at the organizational level
Bibliographic record
Abstract
Many assessment models (AMs) supporting the evaluation of Building Information Modeling (BIM) deployment within an organization or a project have recently been developed. However, it has become challenging to choose which AM is best suited to meet a user's needs in a specific context. These AMs use different criteria and apply different theoretical bases. Moreover, many have been developed with little industry involvement, feedback being given through surveys and focus groups. Lastly, they generally do not consider the context in which the assessment takes place. This paper presents a framework for a type 3 meta-evaluation assessing BIM-AMs concerning their effectiveness, relevance, and usefulness in the context in which they will be implemented. As a contribution to theory, it incorporates the criteria for BIM evaluation developed by Succar et al. (2012) and psychometric validities to evaluate theoretically different models and to incorporate industry input. As a contribution to practice, the framework was used to evaluate two BIM-AMs and provide suggestions for their improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".