Representing Crowd Knowledge: Guidelines for Conceptual Modeling of User-generated Content
Bibliographic record
Abstract
Organizations’ increasing reliance on externally produced information, such as online user-generated content (UGC) and crowdsourcing, challenges common assumptions about conceptual modeling in information systems (IS) development. We demonstrate UGC’s societal importance, analyze its distinguishing characteristics, identify specific conceptual modeling challenges in this setting, evaluate traditional and recently proposed approaches to modeling UGC, propose a set of conceptual modeling guidelines for developing IS that harness structured UGC, and demonstrate how to implement and evaluate the proposed guidelines using a case of development of a real crowdsourcing (citizen science) IS. We conclude by considering implications for conceptual modeling research and practice.
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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 | 0.046 | 0.106 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".