Value co‐creation through collective intelligence in the public sector
Bibliographic record
Abstract
Purpose On the basis of the Collective Intelligence Genome framework, which was developed to describe private, for profit ventures, this study aims to review the recent public sector initiatives launched by the American federal government and the European Union. The study's goal is to examine if, and how, the Genome construct would apply to not for profit. Design/methodology/approach This paper builds on an existing classification methodology for collective intelligence initiatives and extends it to pubic sector initiatives. Findings The findings suggest that, although the framework offers a generally good fit, it does not fully address all the factors at play and the paper proposes expanding the gene pool. In addition, it confirms that Collective Intelligence initiatives do indeed co‐create value and conform to the emerging services dominant logic concept. Originality/value With the growing success of profit motivated internet‐based collaborative ventures, including Innocentive, VenCorps, Threadless and many others, governments have taken notice and engaged. Recent public sector initiatives, including Open.gov, Peer 2 Patent, innovation.ED.gov among others, have begun to leverage collaborative internet media through similar means. These initiatives not only engage a broader community in the co‐creation of value, but also foster what has been termed as Collective Intelligence. This paper details one of the first forays into what might be termed sense making within the public sector usage of Collective Intelligence using the Genome framework and, as such, provides researchers and practitioners with a means of assessing value, potential impact and making comparisons.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".