A Research Framework for Work Sampling and Its Application in Developing Comparative Direct and Support Activity Proportions for Different Trades
Bibliographic record
Abstract
Work sampling has been used to indirectly measure crew productivity. Although favoured for being less costly, easy to adopt, and able to provide quick information, previous work sampling studies have not gone beyond identifying the direct, support, and delay proportions of activities to provide a reasonable estimation of productivity. Research into identifying a relationship between work sampling results and productivity has been limited, and an approach to identify the most productive proportion of direct and support activities for different trades has not been developed. This paper proposes a research framework for crew-based work sampling, supplemented by foreman delay surveys and craftsman questionnaires, to establish a relationship between work sampling and productivity, and to identify the effective proportions of direct and support activities for different trades. The paper describes the development of this framework and illustrates the analysis involved by using case study data. Ultimately, this framework will be used to develop a crew-level productivity analysis model, based on subjective and objective factor modeling, supplemented by work study methods, including work sampling.
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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 | 0.088 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".