Mobile crowdsourcing-based data collection for user-centered facility maintenance management
Bibliographic record
Abstract
Current facility maintenance management (FMM) practices rely heavily on data collection by facility management professionals and FMM systems and, at times, results in inefficiency in facility condition data collection and decision-making for FMM. This is partially because data collection enabled by existing FMM systems lacks (1) top-down information solicitation on facility conditions, such as crowdsourcing task division, and (2) geo-referenced occupant feedback data. Mobile crowdsourcing has great potential to improve current FMM practices, especially in terms of timely data collection. In this context, this study explores the feasibility of mobile crowdsourcing for FMM data collection and highlights the associated opportunities and challenges. A survey was conducted on a university campus to gain an understanding of the human, data, system, geospatial, and automation characteristics of mobile crowdsourcing for FMM data collection on post-secondary campuses. The survey results were confirmed by FM professionals through a focus group discussion and analyzed to reveal the challenges and recommendations for mobile crowdsourcing for user-centered FMM. A conceptual framework is proposed to apply mobile crowdsourcing to the FMM. This research contributes to the body of knowledge by synthesizing the challenges and opportunities associated with mobile crowdsourcing-based data collection for facility maintenance and providing a framework for its application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".