Click here to agree: Managing intellectual property when crowdsourcing solutions
Bibliographic record
Abstract
Tapping into the creativity of a crowd can provide a highly efficient and effective means of acquiring ideas, work, and content to solve problems. But crowdsourcing solutions can also come with risks, including the legal risks associated with intellectual property. Therefore, we raise and address a two-part question: Why—and how—should organizations deal with intellectual property issues when engaging in the crowdsourcing of solutions? The answers lie in understanding the approaches for acquiring sufficient intellectual property from a crowd and limiting the risks of using that intellectual property. Herein, we discuss the hazards of not considering these legal issues and explain how managers can use appropriate terms and conditions to balance and mitigate the risks associated with soliciting solutions from a crowd. Based on differences in how organizations acquire intellectual property and limit associated risks, we identify and illustrate with examples four approaches for managing intellectual property (passive, possessive, persuasive, and prudent) when crowdsourcing solutions. We conclude with recommendations for how organizations should use and tailor the approaches in our framework to source intellectual property from a crowd.
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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 | 0.013 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.231 | 0.074 |
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".