Maintenance Generators and Procurement Methods of Sport Facilities in the North Central Nigeria
Bibliographic record
Abstract
This study assessed the factors affecting maintenance of the various elements of both indoor and outdoor sports facilities as well as the methods used in of maintaining them. Twenty questionnaires were administered to managers and maintenance officers of the sports facilities in the North Central Nigeria. Relevant factors which where peculiar to each of the facilities where obtained from related literatures and validated. The respondents assessed the effects of the factors on the maintenance of the various facilities. Also, the respondent’s opinion was sort on issues relating to methods used for procurement of maintenance works for the sport facilities, and whether there are variations in efficiency of the facilities based on the procurement methods adopted. The results obtained where analyzed using the Relative Importance Index (RII) and simple percentages. RII was used to determine the degree of significance of each factor and how significantly each element of the sport facility is either being insourced or outsourced. From the results, the effect of the factors on the maintenance of the facilities ranged from “not significant” to “very significant”, depending on the facilities in question. The procurement methods used for maintenance works was mostly a combination of insourcing and outsourcing. It was observed that variations exist in the efficiency of maintenance depending on the method of procurement adopted too. It was recommended that more attention be given to the factors affecting maintenance rather than of attempting to address both at the same time. Also it was recommended that the method of procurement of maintenance works should be dependent on the competencies of the in-house staff available and not by taking such decisions without taking into considerations the capabilities present in-house.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".