Practical approach to knowledge management implementation in historic buildings restoration
Bibliographic record
Abstract
The aim of this investigation is to establish an applicable model for knowledge management implementation that works suitably in organizations involved in projects defined for a historic building such as restoration.Based on a peer review on general approaches in literature, some main improvement potentials are recognized to enable the model to meet with the especial objectives, which shall be considered in such projects.Content analysis was applied to ascertain the new presented model capabilities and improvement in each of three main core components of knowledge management implementation model including peoples, processes, and technologies.We came up with a new conceptual model named as Finger Frame Model with four individual tiers connected with an intelligent communication medium .It is concluded that three main components of KM block diagrams consist of knowledge identification, acquisition, and prevention are appropriately covered in basic tier.The other three components of inner cycle of KM basic diagram consist of using, development, and distribution are also covered in an interface between operators and basic tiers, which together with make lower frame of new presented model.The model is in a continuous improving stage considering self-monitoring processes in an individual tier with the same name, which is located in the upper frame.An upper layer named as goal tier is also located in top frame in which, the main quantitative goals of KM implementation in the project organization are considered.The conceptual diagrams as well as internal processes of each main tier are also presented and discussed.Based on the overall explanations, some main benefits and risks are highlighted for new presented model and outlooks are debated.The model is applied in a real case study and the results are compared with the previous knowledge management implementation model.The results show that the model works efficiently in projects organization related to the historic building restoration.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".