Value co‐creation through collective intelligence in the public sector
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it