Bibliographic record
Abstract
To begin to understand the implications of the implementation of IT-mediated Crowds for Politics and Policy purposes, this research builds the first-known dataset of IT-mediated Crowd applications currently in use in the governance context. Using Crowd Capital theory and governance theory as frameworks to organize our data collection, we undertake an exploratory data analysis of some fundamental factors defining this emerging field. Specific factors outlined and discussed include the type of actors implementing IT mediated Crowds in the governance context, the global geographic distribution of the applications, and the nature of the Crowd derived resources being generated for governance purposes. The findings from our dataset of 209 on-going endeavours indicates that a wide-diversity of actors are engaging IT-mediated Crowds in the governance context, both jointly and severally, that these endeavours can be found to exist on all continents, and that said actors are generating Crowd-derived resources in at least ten distinct governance sectors. We discuss the ramifications of these and our other findings in comparison to the research literature on the private-sector use of IT-mediated Crowds, while highlighting some unique future research opportunities stemming from our work.Prpić, J., & Shukla, P. (2014). Crowd Capital in Governance Contexts. Oxford Internet Institute, University of Oxford - IPP 2014 - Crowdsourcing for Politics and Policy.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".