Click here to agree: Managing intellectual property when crowdsourcing solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it