Exploration of Knowledge Acquisition Techniques in Tunnel Industry: The Case Study of Iran Tunnel Association
Bibliographic record
Abstract
Purpose: The main propose of this study is determining appropriate knowledge acquisition techniques to extracttunnel expert’s knowledge.Design/methodology/approach: A porposive sampling method was used and data were collected viaface-to-face interview based on a validated Knowledge Acquisition Questionnaire (KAQ). A total of 33 expertsin tunnel industry who were presented by Iranian Tunnel Associated were identified and selected.Findings: The result of this study showed that semi-structured interview, timeline, think aloud problem-solving,commentary, teach back, concept map, process map, repertory grid technique, composition ladder, decisionladder, process ladder interview, matrix, Observational techniques have meaningful effects on elicitation oftunnel experts’ knowledge.Limitation: Briefly, problem of this project were large number of experts, Limited time for interviews, (Inaccordance to these experts’ avocations and huge responsibility), and outspread geographical distribution (fromTehran, Khorasan, Khoozestan). Inadequate many experts know little about KM and it's advantagesOriginality/value: The innovation of this research is the first time this kind of research has been done in Iran.Until now, in Iran any working has not been done in the field of management and extraction knowledge experts.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".