Bibliographic record
Abstract
In today’s fast-changing business environment, companies and stakeholders are confronted with a new range of businessdecisions. These decisions are constrained by events that are out of the scope of competencies of the decision makers. In thiscontext, prediction markets, which have demonstrated their efficiency in predicting the outcome of major elections, couldsupport companies in leveraging the knowledge and competencies of their collaborators. Using the power of Web 2.0 and theparticipative collaboration of the crowd, prediction markets are well designed to assess this new range of decisions. Thesimple buy and sell mechanism uses the crowd as proxy toward various information sources. Moreover, the decision task isdistributed between the traders, leaving them the tasks of framing the problem, setting the evaluation criteria and collectingthe supporting information, aggregating their results through the current market price.This paper presents the capabilities of prediction market as Web 2.0 decision making tool to increase the efficiency of thedecision making process in Enterprise 2.0 companies. It relies on many characteristics of these companies such as theenhanced collaboration between dispersed collaborators through social networks tools, the commitment of all collaborators inthe knowledge management process and the dedication of the organization toward open and participatory decision processes.It also have the ability to promptly leverage pieces of information scattered through the organization, without requesting asingle stakeholder knowing what to search for and where to search it. Moreover, due to the engagement of a large crowd inthe decision process, final decisions are subject to a broaden adhesion within the company.We conclude by making propositions regarding the deployment of this decision making tool. This involves numerousmanagerial issues, but also opens new perspectives to build upon the collective knowledge of the enterprise.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".