Bridging the Gap between Academic and Practice Quantity Surveying in Nigerian Construction Industry
Bibliographic record
Abstract
In the construction industry worldwide there is a recognizable gap between academics construction knowledge and its application, and construction knowledge and application as seen in field of practice. In Nigeria, the Quantity surveying profession like other professions in the industry, suffers this same fate. It is based on this that this paper identified the possible factors causing gap between the two divisions. A survey approach was adopted, and questionnaire was used to gather data from construction professionals both in practice and in academics, Ondo State. Data gathered were analyzed using percentage, mean item score and Mann-Whitney U Test. Findings revealed that the major factors contributing to gap are majorly more theoretical knowledge than practical, inadequacy in educational curriculum, slow adoption of innovations and inadequate trained personnel. The two categories of respondents( practicing respondents and those in academics) believes that the most important factor that can help and in bridge the gap between QS in Academics and QS in practice is frequent organizing workshop, lectures and seminars which has been identified by past literature. Openness to new innovations, equilibrium of theoretical and practical knowledge, are the closely following factors. This study contributes to body of knowledge on this subject of discuss.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".