Bibliographic record
Abstract
Public and private investments are increasingly being directed towards the development of ICTs for the construction of more inclusive and connected communities. Labelled as Collective Awareness Platforms (CAPs) under the European Seventh Framework Program, these initiatives explore the possibility of tackling societal issues relying on digitally-mediated citizen cooperation. As their diffusion increases, it is important to critically reflect on the extent to which they can effectively trigger forms of engagement and sustainable collaboration within and through digital artefacts. Among the associated risks is the furthering of a technocratic understanding of how collaborative processes work, based on the assumption that the introduction of CAPs would be a sufficient condition for the construction of inclusive and engaged communities. In this respect, this contribution investigates a case in which a digital platform was implemented with the aim of promoting citizens’ deliberation on urban-related issues. This experiment is analyzed by 1) assessing whether the platform functioned as a deliberative space; 2) tracking the negotiation processes of the digital artefacts’ functionalities occurring among initiative’s organizers, platform developers, and participants. The goal of the paper is to understand how different understandings and unexpected usages of the digital platform affected the deliberation process and therefore the initiative’s outcomes.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.091 | 0.049 |
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".