Gathering and evaluating innovation ideas using crowdsourcing: Impact of the idea title and the description on the number of votes in each phase of a two‐phase crowdsourcing project
Bibliographic record
Abstract
Abstract Organizations are using crowdsourcing to capture innovation knowledge from the crowd in the form of ideas and then using the crowd to evaluate those ideas using votes. In this paper, we investigate a crowdsourcing setting in which Canada solicited information from its citizens to develop a digital transformation strategy. Canada used a two‐phase approach. Phase 1 was used to determine which ideas had the largest number of crowd votes, whereas in Phase 2, the crowd voted on the 30 leading vote‐getting ideas to determine the three winning ideas. This research investigates the ability to use information from ideas to estimate the number of votes that the ideas generate. This approach could be used to estimate the number of ideas, before making information available to the crowd. The unstructured text information in the idea is structured by using target concept dictionaries, which are used to estimate the extent to which the dictionary words appear in the ideas (e.g., “globalism”) and are related to the number of votes. Using this approach, roughly 1% of the total words are used to explain roughly 60% of the variance in the votes. Further, we also find that the variables associated with Phase 1 votes are not the same variables associated with Phase 2 votes; that is, the decision‐making variables changed. Finally, we find that votes are statistically significantly related to the content in the idea titles and the idea statements.
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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 | 0.037 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".