Who Owns Ideas? An Investigation of Employees’ Beliefs about the Legal Ownership of Ideas
Bibliographic record
Abstract
When employees believe that they, and not their employers, have legal ownership of ideas, they may choose to keep their ideas from employers, thereby hindering their employers’ ability to produce new products and services. This research used a combination of quantitative and qualitative research methods in order to investigate what factors influence employees’ beliefs about who legally owns ideas. The major findings were as follows. Employees’ beliefs about who owns ideas are influenced directly by employees’ beliefs about the strength of their own legal claim to ideas and the strength of the competing legal claim of their employers. Those two variables are in turn influenced by factors that are specific to each idea, including the degree of employer involvement in the origins of ideas and the nature of the ideas; and by general factors including employees’ beliefs about their job responsibilities and their familiarity with pertinent organizational procedures. Employers should try to strengthen their legal claims to ideas by ensuring they are involved when ideas are originally generated, by socializing employees to believe that their job responsibilities include assigning ownership of ideas to employers, and by ensuring that employees are familiar with relevant organizational procedures.
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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 | 0.030 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".